# pos

id: cord-034824-eelqmzdx
pos: NNP
token:
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lemma:
  https://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy


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id: cord-003297-fewy8y4a
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id: cord-003297-fewy8y4a
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entity:
  A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs


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  the International WIC Institute College of Computer Science and Technology Beijing University of Technology


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  Key Messages for Social Mobilization and Community Engagement in Intense Transmission Areas Comparison


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  An Approach for Chinese Character Captcha Recognition Using CNN Image Flip Captcha Zero Shot Learning


id: cord-312817-gskbu0oh
type: ORG
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  Censoring Outdegree Compromises Inferences of Social Network Peer Effects and Autocorrelation Social


id: cord-303197-hpbh4o77
type: GGP
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  Zinsstag e g 18 19 20 Figure 4 illustrates the co citation network of authors Four clusters of authors emerged in the network green zoonoses


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type: ORG
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  Practice Journal of Applied Psychology Journal of Small Business Management and Family Business Review


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  Multiplex Networks Basic Formalism and Structural Properties Social Network Analysis Methods and Applications Multiplexity


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  The Oxford Handbook of Leadership and Organizations Supply Network Structure Visibility and Risk Diffusion


id: cord-018054-w863h0d3
type: ORG
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  the Barabási Model of human dynamics Impact of Non Poissonian Activity Patterns on Spreading Processes Email Virus Propagation Modeling and Analysis


id: cord-020885-f667icyt
type: GGP
entity:
  metapath m arg max θ v Vm a Am ca Na v log p c a v θ The original Metapath2vec model 6 uses multiple metapaths 29


id: cord-273941-gu6nnv9d
type: PROTEIN
entity:
  alkaline phosphatase ALPL carbonic anhydrase 7 CA7 coagulation factor X F10 DNA repair protein RAD51 homolog 1 RAD51 GSTM1 protein GSTM1 beta secretase


id: cord-273941-gu6nnv9d
type: PROTEIN
entity:
  proteinase 3CL PRO arginase beta lactamase AmpC glutathione reductase M18 aspartyl aminopeptidase malate dehydrogenase


id: cord-273941-gu6nnv9d
type: GGP
entity:
  RAD51 GSTM1 protein GSTM1 beta secretase 1 BACE1 plasminogen activator inhibitor 1 SERPINE1 prothrombin F2 regulator of G protein


id: cord-273941-gu6nnv9d
type: GGP
entity:
  quercetin 1 2 3 4 6 pentagalloyoglucose 1 2 3 6 tetrakis O galloyl beta D glucose bellericagenin A1 and prodelphinidin A1


id: cord-273941-gu6nnv9d
type: DISEASE
entity:
  punicalins terflavin B 4 O S flavogallonyl 6 O galloylbeta D glucopyranose and 4 6 O S S gallagyl alpha beta D glucopyranose


id: cord-230294-bjy2ixcj
type: CL
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  stem abband 3 Draw links between a stemmed word and its subsequent one Store the resulting edge list of word co occurrences 4 Sentences containing a negation i e not underwent an additional step parsing their syntactic structure This was done in order to identify the target of negation e g in this is not peace the negation refers to peace Syntactic dependencies were not used for network construction but intervened in emotional profiling instead see below The resulting word network also captures syntactic dependencies between words 16 related by online users to a specific hashtag or combination of hashtag We used closeness centrality to detect the relevance of words for a given hashtag Text pre processing such as word stemming and syntactic dependencies was performed using Mathematica 11 3


id: cord-253711-a0prku2k
type: CELL_LINE
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  NetLog and Repast Robertson 2005 With regard to the second assumption both the infection and communication networks


id: cord-133273-kvyzuayp
type: ORG
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  Keywords Computational Materials Science Property Structure Mapping Texture Evolution Optimization Machine Learning Reinforcement Learning


id: cord-133273-kvyzuayp
type: ORG
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  Medicine DICOM Standard National Electrical Manufacturers Association CT Realistic Lung Nodule Simulation


id: cord-133273-kvyzuayp
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  Past Present and Future A Survey on Specialised Hardware for Machine Learning A Survey on Distributed Machine Learning Hardware


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  Wearable Stress and Affect Detection Feasibility and Usability Aspects of Continuous Remote Monitoring of Health Status


id: cord-133273-kvyzuayp
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  Towards International Standards for the Evaluation of Artificial Intelligence for Health Proposed Regulatory Framework for Modifications to Artificial Intelligence Machine Learning


id: cord-133273-kvyzuayp
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  A Comparative Analysis of Cross Correlation Matching Algorithms Using a Pyramidal Resolution Approach Fast


id: cord-133273-kvyzuayp
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  A Large Dataset to Train Convolutional Networks for Disparity Optical Flow and Scene Flow Estimation Pyramid Stereo Matching Network Microstructure


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  i e 10 6 s 34 However an important question on biological subdiffusion may require higher time resolution to be solved This


id: cord-276178-0hrs1w7r
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  Futuristic Trends in Network and Communication Technologies Futuristic Trends in Networks and Computing Technologies Communications in Computer and Information Handbook of Wireless Sensor Networks Issues and Challenges in Current Scenario s Lecture Notes in Networks and Systems 121 Proceedings of ICRIC 2019 Introduction on Wireless Sensor Networks Issues and Challenges in Current Era


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  the multicenter international Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study DESIGN Prospective


id: cord-307735-6pf7fkvq
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entity:
  the Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study


id: cord-307735-6pf7fkvq
type: ORG
entity:
  CONCLUSIONS The Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study


id: cord-307735-6pf7fkvq
type: ORG
entity:
  the multicenter international Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study Design Prospective


id: cord-307735-6pf7fkvq
type: ORG
entity:
  Conclusions The Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study


id: cord-307735-6pf7fkvq
type: ORG
entity:
  the multicenter international Society of Critical Care Medicine SCCM Discovery Network Viral Infection and


id: cord-266771-zesp6q0w
type: DNA
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  sourc e gbs_ge_summa ry_r cad 0 v onepa ge q f false Vie Physarale Roman Roads with Slime Mould Paris chez Mme


id: cord-332313-9m2iozj3
type: WORK_OF_ART
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  All Organizations Are Public Bridging Public and Private Organizational Theories Indicators for the dynamics of research


id: cord-332313-9m2iozj3
type: WORK_OF_ART
entity:
  The Impact of the Crisis on Research and Innovation Policies in European Commission DG Research Reinventing


id: cord-333088-ygdau2px
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  e c o l o g i c a l c o m p l e x i t y 3 2 0 0 6 8 0 9 0 conclusions hold under an explicit modeling framework that binds together the social dynamics of both prey and predator


id: cord-343419-vl6gkoin
type: EVENT
entity:
  Chinese Academy of Engineering Physics Huaqiao University Hong Kong Polytechnic University Tsing Hua University


id: cord-343419-vl6gkoin
type: ORG
entity:
  Science and Technology Policy Innovation System Social Network Analysis Knowledge Evolution Science Map Bibliometric


id: cord-343419-vl6gkoin
type: ORG
entity:
  Centrality and Power in Organizations Networks and Organizations Structure Form and Action Centrality


id: cord-343419-vl6gkoin
type: CHEMICAL
entity:
  _ _Phys 6 Nanyang_Technol_Univ Chinese_Acad_Engn_ _Phys Tsing_Hua_Univ 7 Hong_Kong_Polytech_Univ Nanyang_Technol_Univ Univ_Laval 8 Tsing_Hua_Univ Huaqiao_Univ Seoul_Natl_Univ 9


id: cord-340827-vx37vlkf
type: GGP
entity:
  P d ¼ 2d À3 e g see Pastor Satorras and Vespignani 2000 2001 In that case there is always a positive steady state infection rate More generally Lopez Pintado 2008 addresses the question of when it is that there will be a positive steady state infection rate To get some intuition for her results let so that the equation y ¼ H y


id: cord-340827-vx37vlkf
type: ORG
entity:
  Social Structure Segregation and Economic Behavior Social and Economic Networks Relating Network Structure to Diffusion Properties


id: cord-340827-vx37vlkf
type: LAW
entity:
  Design Innovation and Fashion Cycles Complementarities Group Formation and Preferences for Similarity Workers and Wages


id: cord-340827-vx37vlkf
type: EVENT
entity:
  The Laws of Imitation Social Interactions Local Spillovers and Unemployment Identifying Formal and Informal Inuence in Technology Adoption with Network Externalities Medical Innovation Revisited Social Contagion


id: cord-340101-n9zqc1gm
type: ORG
entity:
  the Amsterdam Study of the Elderly AMSTEL Loneliness and Risk of Alzheimer Disease Loneliness Is Associated with Sleep Fragmentation


id: cord-340101-n9zqc1gm
type: FAC
entity:
  Importance of Studying the Contributions of Early Adverse Experience to Neurobiological Findings in Depression The Influence of Social Hierarchy on Primate Health Social


id: cord-346309-hveuq2x9
type: ORG
entity:
  the Centre for Epidemiology and Research New South Wales Department of Health New South Wales Emergency Department Data Collection


id: cord-346309-hveuq2x9
type: ORG
entity:
  Centers for Disease Control and Prevention called Framework for Evaluating Public Health Surveillance Systems


id: cord-338127-et09wi82
type: ORG
entity:
  Merge Convolutional Neural Network for Fast Vehicle Detection in Urban Traffic Surveillance A Deep Network Architecture for Super Resolution Aided Hyperspectral Image Classification With Classwise Loss


# url

id: cord-034824-eelqmzdx
domain: github.com
url:
  http://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy


# bib

id: cord-000196-lkoyrv3s
title: Dynamics and Control of Diseases in Networks with Community Structure
author: Salathé, Marcel
date: 2010-04-08
txt: ./txt/cord-000196-lkoyrv3s.txt
cache: ./cache/cord-000196-lkoyrv3s.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851561/
doi: 10.1371/journal.pcbi.1000736
summary:
  Running standard susceptible-infected-resistant (SIR) epidemic simulations (see Methods) on these networks, we find that the average epidemic size, epidemic duration and the peak prevalence of the epidemic are strongly affected by a change in community structure connectivity that is independent of the overall degree distribution of the full network ( Figure 1 ). While infections are most likely to spread along the shortest paths between any two nodes, the cumulative contribution of other paths can still be important [40] : immunization strategies based on random walk centrality result in the lowest number of infected cases at low vaccination coverage (Figure 4b and 4c ). In practice, identifying immunization targets may be impossible using such algorithms, because the structure of the contact network relevant for the spread of a directly transmissible disease is generally not known.

abstract:
  The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.


id: cord-002929-oqe3gjcs
title: Mapping road network communities for guiding disease surveillance and control strategies
author: Strano, Emanuele
date: 2018-03-16
txt: ./txt/cord-002929-oqe3gjcs.txt
cache: ./cache/cord-002929-oqe3gjcs.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856805/
doi: 10.1038/s41598-018-22969-4
summary:
  We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance. falciparum malaria prevalence and population data with road networks for weighted community detection. falciparum malaria prevalence and population (Fig. 5a ) through weighting road links by the maximum values across them produces a different pattern of communities (Fig. 5b) to those based solely on network structure (Fig. 3) .

abstract:
  Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies. While analyses have been undertaken to identify and map connectivity in global air, shipping and migration networks, such analyses have yet to be undertaken on the road networks that carry the vast majority of travellers in low and middle income settings. Here we present methods for identifying road connectivity communities, as well as mapping bridge areas between communities and key linkage routes. We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance.


id: cord-003297-fewy8y4a
author: Wang, Ming-Yang
date: 2018-09-18
txt: ./txt/cord-003297-fewy8y4a.txt
cache: ./cache/cord-003297-fewy8y4a.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6225272/
doi: 10.3390/molecules23092385
summary:
  A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The contour maps around aconitine alkaloids generated by comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were combined with the interactions between ligand substituents and amino acids obtained from docking results to gain insight on the relationship between the structure of aconitine alkaloids and their toxicity. Finally, we combined the ligand-based 3D-QSTR analysis with the structure-based molecular docking study to identify the necessary moiety related to the cardiotoxicity mechanism of the aconitine alkaloids (in Figure 10 ).

title:
  A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs

abstract:
  A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.


id: cord-003887-4grjr0h3
title: Unified feature association networks through integration of transcriptomic and proteomic data
author: McClure, Ryan S.
date: 2019-09-17
txt: ./txt/cord-003887-4grjr0h3.txt
cache: ./cache/cord-003887-4grjr0h3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748406/
doi: 10.1371/journal.pcbi.1007241
summary:
  We show that these networks, including the cross-type edges in the network, are accurate, and we use this approach to interrogate and compare networks inferred from data derived from antibodymediated entry of Dengue virus into cells and from receptor-mediated entry. While a number of the mutual information based methods improved upon PCC in drawing cross-type edges, GENIE3, the random forest method, was by far the best method for creating integrated networks (Fig 2A) . Having shown with our analysis of Dengue virus infection that GENIE3 is the inference method that is best able to create highly integrated and accurate networks of proteomic and transcriptomic data we applied this approach to comparison of networks derived from receptor-mediated Dengue virus infection and antibody-mediated Dengue virus infection. Despite these challenges and the small number of cross-type edges, GENIE3 does emerge as the best method for inferring integrated networks, specifically of proteomic and transcriptomic data.

abstract:
  High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different–omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.


id: cord-005090-l676wo9t
title: Network immunization and virus propagation in email networks: experimental evaluation and analysis
author: Gao, Chao
date: 2010-07-14
txt: ./txt/cord-005090-l676wo9t.txt
cache: ./cache/cord-005090-l676wo9t.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088328/
doi: 10.1007/s10115-010-0321-0
summary:
  For example, computer scientists focus on algorithms and the computational complexities of strategies, i.e. how to quickly search a short path from one "seed" node to a targeted node just based on local information, and then effectively and efficiently restrain virus propagation [42] . Section 4 describes the experiments which are performed to compare different immunization strategies with the measurements of the immunization efficiency, the cost and the robustness in both synthetic networks (including a synthetic community-based network) and two real email networks (the Enron and a university email network), and analyze the effects of network structures and human dynamics on virus propagation. It is readily to observe the microscopic process of worm propagating through this model, and uncover the effects of different factors (e.g. the power-law exponent, human dynamics and the average path length of the network) on virus propagation and immunization strategies.

abstract:
  Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics.


id: cord-006292-rqo10s2g
title: Bonded-communities in HantaVirus research: a research collaboration network (RCN) analysis
author: Kumar, Sameer
date: 2016-04-07
txt: ./txt/cord-006292-rqo10s2g.txt
cache: ./cache/cord-006292-rqo10s2g.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101558/
doi: 10.1007/s11192-016-1942-1
summary:
  title: Bonded-communities in HantaVirus research: a research collaboration network (RCN) analysis We apply research collaboration network analysis to investigate the best-connected authors in the field. Significant correlation was found between author''s structural position in the network and research performance, thus further supporting a well-studied phenomenon that centrality effects research productivity. Thus, in addition to common bibliometric analyses (i.e. annual paper production, average citations, top papers, number of papers per country, author research productivity, etc.), the present study has the following main objectives: a. The study has significance as this would be perhaps one of the first studies to investigate research performance and bonded communities in hantavirus research from the perspective of research collaborations and networks. In this section, we investigate if the connectedness and relative position of authors have effect on the research performance and then analyze bonded communities embedded in coauthorship networks.

abstract:
  Hantavirus, one of the deadliest viruses known to humans, hospitalizes tens of thousands of people each year in Asia, Europe and the Americas. Transmitted by infected rodents and their excreta, Hantavirus are identified as etiologic agents of two main types of diseases—Hemorrhagic fever with renal syndrome and hantavirus pulmonary syndrome, the latter having a fatality rate of above 40 %. Although considerable research for over two decades has been going on in this area, bibliometric studies to gauge the state of research of this field have been rare. An analysis of 2631 articles, extracted from WoS databases on Hantavirus between 1980 and 2014, indicated a progressive increase (R (2) = 0.93) in the number of papers over the years, with the majority of papers being published in the USA and Europe. About 95 % papers were co-authored and the most common arrangement was 4–6 authors per paper. Co-authorship has seen a steady increase (R (2) = 0.57) over the years. We apply research collaboration network analysis to investigate the best-connected authors in the field. The author-based networks have 49 components (connected clump of nodes) with 7373 vertices (authors) and 49,747 edges (co-author associations) between them. The giant component (the largest component) is healthy, occupying 84.19 % or 6208 vertices with 47,117 edges between them. By using edge-weight threshold, we drill down into the network to reveal bonded communities. We find three communities’ hotspots—one, led by researchers at University of Helsinki, Finland; a second, led by the Centers of Disease Control and Prevention, USA; and a third, led by Hokkaido University, Japan. Significant correlation was found between author’s structural position in the network and research performance, thus further supporting a well-studied phenomenon that centrality effects research productivity. However, it was the PageRank centrality that out-performed degree and betweenness centrality in its strength of correlation with research performance.


id: cord-007415-d57zqixs
title: Correlations between structure and random walk dynamics in directed complex networks
author: da Fontoura Costa, Luciano
date: 2007-07-30
txt: ./txt/cord-007415-d57zqixs.txt
cache: ./cache/cord-007415-d57zqixs.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112555/
doi: 10.1063/1.2766683
summary:
  They establish the necessary conditions for networks to be topologically and dynamically fully correlated (e.g., word adjacency and airport networks), and show that in this case Zipf''s law is a consequence of the match between structure and dynamics. They establish the necessary conditions for networks to be topologically and dynamically fully correlated ͑e.g., word adjacency and airport networks͒, and show that in this case Zipf''s law is a consequence of the match between structure and dynamics. 2766683͔ We address the relationship between structure and dynamics in complex networks by taking the steady-state distribution of the frequency of visits to nodes-a dynamical feature-obtained by performing random walks 1 along the networks. In addition to providing a modeling approach intrinsically compatible with dynamics involving successive visits to nodes by a single or multiple agents, such as is the case with world wide web ͑WWW͒ navigation, text writing, and transportation systems, random walks are directly related to diffusion.

abstract:
  In this letter the authors discuss the relationship between structure and random walk dynamics in directed complex networks, with an emphasis on identifying whether a topological hub is also a dynamical hub. They establish the necessary conditions for networks to be topologically and dynamically fully correlated (e.g., word adjacency and airport networks), and show that in this case Zipf’s law is a consequence of the match between structure and dynamics. They also show that real-world neuronal networks and the world wide web are not fully correlated, implying that their more intensely connected nodes are not necessarily highly active.


id: cord-007708-hr4smx24
title: Taking Bioinformatics to Systems Medicine
author: van Kampen, Antoine H. C.
date: 2015-08-13
txt: ./txt/cord-007708-hr4smx24.txt
cache: ./cache/cord-007708-hr4smx24.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120931/
doi: 10.1007/978-1-4939-3283-2_2
summary:
  Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. To enable systems medicine it is necessary to characterize the patient at various levels and, consequently, to collect, integrate, and analyze various types of data including not only clinical (phenotype) and molecular data, but also information about cells (e.g., disease-related alterations in organelle morphology), organs (e.g., lung impedance when studying respiratory disorders such as asthma or chronic obstructive pulmonary disease), and even social networks. Bioinformatics covers many types of analyses including nucleotide and protein sequence analysis, elucidation of tertiary protein structures, quality control, pre-processing and statistical analysis of omics data, determination of genotypephenotype relationships, biomarker identifi cation, evolutionary analysis, analysis of gene regulation, reconstruction of biological networks, text mining of literature and electronic patient records, and analysis of imaging data.

abstract:
  Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.


id: cord-010751-fgk05n3z
title: Objective measures for sentinel surveillance in network epidemiology
author: Holme, Petter
date: 2018-08-15
txt: ./txt/cord-010751-fgk05n3z.txt
cache: ./cache/cord-010751-fgk05n3z.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217546/
doi: 10.1103/physreve.98.022313
summary:
  Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Furthermore, we do not find one type of network structure that predicts the objective measures, i.e., that depends both on the data set and the SIR parameter values. Finally, if the objective is to stop the disease as early as possible, it makes sense to measure the time to extinction or detection (infection of a sentinel) [13] . Just as for the case of static networks, τ (t x , f d ) is always nonpositive, meaning the time to detection or extinction ranks the nodes in a way positively correlated with the frequency of detection. In Fig. 4 , we show the correlation between our three objective measures and the structural descriptors as a function of β for the Office data set.

abstract:
  Assume one has the capability of determining whether a node in a network is infectious or not by probing it. Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures from the literature quantifying the performance of nodes in sentinel surveillance: the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. This means sentinel surveillance is a class of problems, and solutions need to chose an objective measure for the particular scenario in question. As opposed to other problems in network epidemiology, we draw similar conclusions from the static and temporal networks. Furthermore, we do not find one type of network structure that predicts the objective measures, i.e., that depends both on the data set and the SIR parameter values.


id: cord-010758-ggoyd531
title: Epidemic Threshold in Continuous-Time Evolving Networks
author: Valdano, Eugenio
date: 2018-02-06
txt: ./txt/cord-010758-ggoyd531.txt
cache: ./cache/cord-010758-ggoyd531.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219439/
doi: 10.1103/physrevlett.120.068302
summary:
  A vast array of theoretical results characterize the epidemic threshold [14] , mainly under the limiting assumptions of quenched and annealed networks [4, [15] [16] [17] [18] , i.e., when the time scale of the network evolution is much slower or much faster, respectively, than the dynamical process. Departing from traditional approximations, few novel approaches are now available that derive the epidemic threshold constrained to specific contexts of generative models of temporal networks [22, 32, 35, [38] [39] [40] [41] or considering generic discrete-time evolving contact patterns [42] [43] [44] . Our approach yields a solution for the threshold of epidemics spreading on generic continuously evolving networks, and a closed form under a specific condition that is then validated through numerical simulations. By mapping the system into a multilayer structure encoding both network evolution and diffusion dynamics, the infection propagator approach derives the epidemic threshold as the solution of the equation ρ½PðT step Þ ¼ 1 [43, 44] , where ρ is the spectral radius of the following matrix:

abstract:
  Current understanding of the critical outbreak condition on temporal networks relies on approximations (time scale separation, discretization) that may bias the results. We propose a theoretical framework to compute the epidemic threshold in continuous time through the infection propagator approach. We introduce the weak commutation condition allowing the interpretation of annealed networks, activity-driven networks, and time scale separation into one formalism. Our work provides a coherent connection between discrete and continuous time representations applicable to realistic scenarios.


id: cord-011400-zyjd9rmp
title: Network Reconstruction and Community Detection from Dynamics
author: Peixoto, Tiago P.
date: 2019-09-18
txt: ./txt/cord-011400-zyjd9rmp.txt
cache: ./cache/cord-011400-zyjd9rmp.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226905/
doi: 10.1103/physrevlett.123.128301
summary:
  Researchers have approached this reconstruction task from a variety of angles, resulting in many different methods, including thresholding the correlation between time series [6] , inversion of deterministic dynamics [7] [8] [9] , statistical inference of graphical models [10] [11] [12] [13] [14] and of models of epidemic spreading [15] [16] [17] [18] [19] [20] , as well as approaches that avoid explicit modeling, such as those based on transfer entropy [21] , Granger causality [22] , compressed sensing [23] [24] [25] , generalized linearization [26] , and matching of pairwise correlations [27, 28] . [32] proposed a method to infer community structure from time-series data that bypasses network reconstruction by employing a direct modeling of the dynamics given the group assignments, instead. We take two empirical networks, the with E ¼ 39430 edges, and a food web from Little Rock Lake [46] , containing N ¼ 183 nodes and E ¼ 2434 edges, and we sample from the SIS (mimicking the spread of a pandemic) and Ising model (representing simplified interspecies interactions) on them, respectively, and evaluate the reconstruction obtained via the joint and separate inference with community detection, with results shown in Fig. 2 .

abstract:
  We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.


id: cord-015861-lg547ha9
title: The Realization Path of Network Security Technology Under Big Data and Cloud Computing
author: Kang, Nan
date: 2019-03-12
txt: ./txt/cord-015861-lg547ha9.txt
cache: ./cache/cord-015861-lg547ha9.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119949/
doi: 10.1007/978-981-13-7123-3_66
summary:
  title: The Realization Path of Network Security Technology Under Big Data and Cloud Computing This paper studies the cloud and big data technology based on the characters of network security, including virus invasion, data storage, system vulnerabilities, network management etc. Cloud computing is a service that based on the increased usage and delivery of the internet related services, it promotes the rapidly development of the big data information processing technology, improves the processing and management abilities of big data information. In the mobile cloud system model, the grid architecture that relies on local computing resources and the wireless network to build cloud computing, which will select the components of data flow graph to migrate to the cloud, Computer data processing cloud computing formula modeling, fGðV; EÞ; si; di; jg is the given data flow applications, assuming that the channel capacity is infinite, the problem of using cloud computing technology to optimize big data information processing is described as follows maxmax xi;yi;jxi;yi;j

abstract:
  This paper studies the cloud and big data technology based on the characters of network security, including virus invasion, data storage, system vulnerabilities, network management etc. It analyzes some key network security problems in the current cloud and big data network. Above all, this paper puts forward technical ways of achieving network security. Cloud computing is a service that based on the increased usage and delivery of the internet related services, it promotes the rapidly development of the big data information processing technology, improves the processing and management abilities of big data information. With tie rapid development of computer technology, big data technology brings not only huge economic benefits, but the evolution of social productivity. However, serials of safety problems appeared. How to increase network security has been become the key point. This paper analyzes and discusses the technical ways of achieving network security.


id: cord-015967-kqfyasmu
title: Epidemic Models: Their Spread, Analysis and Invasions in Scale-Free Networks
author: Tagore, Somnath
date: 2015-03-20
txt: ./txt/cord-015967-kqfyasmu.txt
cache: ./cache/cord-015967-kqfyasmu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120102/
doi: 10.1007/978-3-319-15916-4_1
summary:
  For instance, hub individuals of such high-risk individuals help in maintaining sexually transmitted diseases (STDs) in different populations where majority belong to long-term monogamous relationships, whereas in case of SARS epidemic, a significant proportion of all infections are due to high risk connected individuals. Likewise, models for epidemic spread in static heavy-tailed networks have illustrated that with a degree distribution having moments resulted in lesser prevalence and/or termination for smaller rates of infection [14] . Generally, epidemic models consider contact networks to be static in nature, where all links are existent throughout the infection course. But, in cases like HIV, which spreads through a population over longer time scales, the course of infection spread is heavily dependent on the properties of the contact individuals. Likewise, for a wide range of scale-free networks, epidemic threshold is not existent, and infections with low spreading rate prevail over the entire population [10] .

abstract:
  The mission of this chapter is to introduce the concept of epidemic outbursts in network structures, especially in case of scale-free networks. The invasion phenomena of epidemics have been of tremendous interest among the scientific community over many years, due to its large scale implementation in real world networks. This chapter seeks to make readers understand the critical issues involved in epidemics such as propagation, spread and their combat which can be further used to design synthetic and robust network architectures. The primary concern in this chapter focuses on the concept of Susceptible-Infectious-Recovered (SIR) and Susceptible-Infectious-Susceptible (SIS) models with their implementation in scale-free networks, followed by developing strategies for identifying the damage caused in the network. The relevance of this chapter can be understood when methods discussed in this chapter could be related to contemporary networks for improving their performance in terms of robustness. The patterns by which epidemics spread through groups are determined by the properties of the pathogen carrying it, length of its infectious period, its severity as well as by network structures within the population. Thus, accurately modeling the underlying network is crucial to understand the spread as well as prevention of an epidemic. Moreover, implementing immunization strategies helps control and terminate theses epidemics.


id: cord-016196-ub4mgqxb
title: Study on Efficient Complex Network Model
author: Wang, Cheng
date: 2012-11-20
txt: ./txt/cord-016196-ub4mgqxb.txt
cache: ./cache/cord-016196-ub4mgqxb.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120410/
doi: 10.1007/978-3-642-35398-7_20
summary:
  This paper summarizes the relevant research of the complex network systematically based on Statistical Property, Structural Model, and Dynamical Behavior. An important discover in the complex network researching is that the average path length of the most of the large-scale real networks is much less than our imagine, which we call ''''Small-world Effect''''. Paul Erdös and Alfred Rényi discovered a complete random network model in the late 50s twentieth century, it is made of any two nodes which connected with probability p in the graph made of N nodes, its average degree is \k [ ¼ pðN À 1Þ % PN; the average path length l : ln N= lnð\k [ Þ; the convergence factor C ¼ P; when the value of N is very large, the distribution of the node degree approximately equals poisson distribution. However, the regular network has aggregation, but its average shortest path length is larger, random graph has the opposite property, having small-world and less convergence factor.

abstract:
  This paper summarizes the relevant research of the complex network systematically based on Statistical Property, Structural Model, and Dynamical Behavior. Moreover, it emphatically introduces the application of the complex network in the economic system.


id: cord-016448-7imgztwe
title: Protein-protein interactions: analysis and prediction
author: Frishman, D.
date: 2009-10-01
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120725/
doi: 10.1007/978-3-211-75123-7_17
abstract:
  Proteins represent the tools and appliances of the cell — they assemble into larger structural elements, catalyze the biochemical reactions of metabolism, transmit signals, move cargo across membrane boundaries and carry out many other tasks. For most of these functions proteins cannot act in isolation but require close cooperation with other proteins to accomplish their task. Often, this collaborative action implies physical interaction of the proteins involved. Accordingly, experimental detection, in silico prediction and computational analysis of protein-protein interactions (PPI) have attracted great attention in the quest for discovering functional links among proteins and deciphering the complex networks of the cell.


id: cord-017423-cxua1o5t
title: A Review of Microblogging Marketing Based on the Complex Network Theory
author: Wang, Rui
date: 2011-11-12
txt: ./txt/cord-017423-cxua1o5t.txt
cache: ./cache/cord-017423-cxua1o5t.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121981/
doi: 10.1007/978-1-4419-8849-2_134
summary:
  Microblogging marketing which is based on the online social network with both small-world and scale-free properties can be explained by the complex network theory. In brief, the complex network theory pioneered by the small-world and scalefree network model overcomes the constraints of the network size and structure of regular network and random network, describes the basic structural features of high clustering coefficient, short average path length, power-law degree distribution, and scale-free characteristics. Generally speaking, microblog has characteristics of the small-world, scale-free, high clustering coefficient, short average path length, hierarchical structure, community structure, and node degree distribution of positive and negative correlation. The complex network characteristics of the small-world, scale-free, high clustering coefficient, short average path length, hierarchical structure, community structure, node degree distribution of positive and negative correlation and its application in various industries provide theoretical and practical methods to conduct and implement microblogging marketing.

abstract:
  Microblogging marketing which is based on the online social network with both small-world and scale-free properties can be explained by the complex network theory. Through systematically looking back at the complex network theory in different development stages, this chapter reviews literature from the microblogging marketing angle, then, extracts the analytical method and operational guide of microblogging marketing, finds the differences between microblog and other social network, and points out what the complex network theory cannot explain. In short, it provides a theoretical basis to effectively analyze microblogging marketing by the complex network theory.


id: cord-018054-w863h0d3
title: Non-poisson Processes of Email Virus Propagation
author: Mirchev, Miroslav
date: 2010
txt: ./txt/cord-018054-w863h0d3.txt
cache: ./cache/cord-018054-w863h0d3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122840/
doi: 10.1007/978-3-642-10781-8_20
summary:
  We propose an email virus propagation model that considers both heavy-tailed intercontact time distribution, and heavy-tailed topology of email networks. In this paper, we propose an email virus propagation model with nonlinear dynamical system, which considers both heavy-tailed intercontact time distribution and heavy-tailed topology of email networks. After that in Section 3, we propose a discrete stochastic model for Non-Poisson virus propagation in email networks with power law topology and have-tail distributed interevent times. We propose a discrete stochastic model for virus propagation in email network with power law topology and communication pattern with heavy-tailed interevent time distribution. First, we compare the spreading of email viruses in power law and random (Erdos-Renyi) network, by using both Poisson process approximation and true interevent distribution. We proposed a model for virus propagation in email network with power law topology and communication pattern with heavy-tailed interevent time distribution.

abstract:
  Email viruses are one of the main security problems in the Internet. In order to stop a computer virus outbreak, we need to understand email interactions between individuals. Most of the spreading models assume that users interact uniformly in time following a Poisson process, but recent measurements have shown that the intercontact time follows heavy-tailed distribution. The non-Poisson nature of contact dynamics results in prevalence decay times significantly larger than predicted by standard Poisson process based models. Email viruses spread over a logical network defined by email address books. The topology of this network plays important role in the spreading dynamics. Recent observations suggest that node degrees in email networks are heavy-tailed distributed and can be modeled as power law network. We propose an email virus propagation model that considers both heavy-tailed intercontact time distribution, and heavy-tailed topology of email networks.


id: cord-019055-k5wcibdk
title: Disease Spreading in Time-Evolving Networked Communities
author: Pacheco, Jorge M.
date: 2017-10-05
txt: ./txt/cord-019055-k5wcibdk.txt
cache: ./cache/cord-019055-k5wcibdk.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7124106/
doi: 10.1007/978-981-10-5287-3_13
summary:
  We show that the effective infectiousness of a disease taking place along the edges of this temporal network depends on the population size, the number of infected individuals in the population and the capacity of healthy individuals to sever contacts with the infected, ultimately dictated by availability of information regarding each individual''s health status. Furthermore, the knowledge an individual has (based on local and/or social media information) about the health status of acquaintances, partners, relatives, etc., combined with individual preventive strategies [42] [43] [44] [45] [46] [47] [48] [49] [50] (such as condoms, vaccination, the use of face masks or prophylactic drugs, avoidance of visiting specific web-pages, staying away from public places, etc.), also leads to changes in the structure and shape of the contact networks that naturally acquire a temporal dimension that one should not overlook.

abstract:
  Human communities are organized in complex webs of contacts that may be represented by a graph or network. In this graph, vertices identify individuals and edges establish the existence of some type of relations between them. In real communities, the possible edges may be active or not for variable periods of time. These so-called temporal networks typically result from an endogenous social dynamics, usually coupled to the process under study taking place in the community. For instance, disease spreading may be affected by local information that makes individuals aware of the health status of their social contacts, allowing them to reconsider maintaining or not their social contacts. Here we investigate the impact of such a dynamical network structure on disease dynamics, where infection occurs along the edges of the network. To this end, we define an endogenous network dynamics coupled with disease spreading. We show that the effective infectiousness of a disease taking place along the edges of this temporal network depends on the population size, the number of infected individuals in the population and the capacity of healthy individuals to sever contacts with the infected, ultimately dictated by availability of information regarding each individual’s health status. Importantly, we also show how dynamical networks strongly decrease the average time required to eradicate a disease.


id: cord-020885-f667icyt
title: Semantic Path-Based Learning for Review Volume Prediction
author: Sharma, Ujjwal
date: 2020-03-17
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148205/
doi: 10.1007/978-3-030-45439-5_54
abstract:
  Graphs offer a natural abstraction for modeling complex real-world systems where entities are represented as nodes and edges encode relations between them. In such networks, entities may share common or similar attributes and may be connected by paths through multiple attribute modalities. In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes. An attention-based mechanism is used to combine multiple attribute-specific representations in a late fusion setup. We focus on a real-world network formed by restaurants and their shared attributes and evaluate performance on predicting the number of reviews a restaurant receives, a strong proxy for popularity. Our results demonstrate the rich expressiveness of such representations in predicting review volume and the ability of an attention-based model to selectively combine individual representations for maximum predictive power on the chosen downstream task.


id: cord-024346-shauvo3j
title: Using Open Source Libraries in the Development of Control Systems Based on Machine Vision
author: Kruglov, Vasiliy N.
date: 2020-05-05
txt: ./txt/cord-024346-shauvo3j.txt
cache: ./cache/cord-024346-shauvo3j.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198258/
doi: 10.1007/978-3-030-47240-5_7
summary:
  The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. To build a neural network and apply machine learning methods, a sample of images of crushed ore stones in gray scale was formed. It is this type of neural network that will be used in constructing a model for recognizing boundary points of fragments of stone images. These modifications of the base convolutional neural network did not lead to an improvement in its performance -all models had the worst quality on the test sample (in the region of 88-90% accuracy). In this work, a convolutional neural network was developed and tested to recognize boundaries on images of crushed ore stones. Based on the drawn borders on the test images, it can be concluded that the convolutional neural network is able to correctly identify the boundary points with a high probability.

abstract:
  The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. The structure of the neural network is given. The construction of training and test datasets of ore particle images is described. Various modifications of the underlying neural network have been investigated. Experimental results are presented.


id: cord-024552-hgowgq41
title: Hydrological Process Surrogate Modelling and Simulation with Neural Networks
author: Zhang, Ruixi
date: 2020-04-17
txt: ./txt/cord-024552-hgowgq41.txt
cache: ./cache/cord-024552-hgowgq41.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206312/
doi: 10.1007/978-3-030-47436-2_34
summary:
  Moreover, we argue that the neural network model, although trained on some example terrains, is generally capable of simulating terrains of different sizes and spatial characteristics. We propose to learn a flood surrogate model by training a neural network with pairs of inputs and outputs from the numerical model. With the trained model from a given data set, the neural network is capable of simulating directly spatially different terrains. Moreover, while a neural network is generally constrained to a fixed size of its input, the model that we propose is able to simulate terrains of different sizes and spatial characteristics. In Case 2, the network is trained and tested with 200 different synthetic DEMs. The data set is generated with Landlab. We propose a neural network model, which is trained with pairs of inputs and outputs of an off-the-shelf numerical flood simulator, as an efficient and effective general surrogate model to the simulator.

abstract:
  Environmental sustainability is a major concern for urban and rural development. Actors and stakeholders need economic, effective and efficient simulations in order to predict and evaluate the impact of development on the environment and the constraints that the environment imposes on development. Numerical simulation models are usually computation expensive and require expert knowledge. We consider the problem of hydrological modelling and simulation. With a training set consisting of pairs of inputs and outputs from an off-the-shelves simulator, We show that a neural network can learn a surrogate model effectively and efficiently and thus can be used as a surrogate simulation model. Moreover, we argue that the neural network model, although trained on some example terrains, is generally capable of simulating terrains of different sizes and spatial characteristics.


id: cord-024571-vlklgd3x
title: Community Analysis of a Crisis Response Network
author: Kim, Yushim
date: 2019-07-28
txt: ./txt/cord-024571-vlklgd3x.txt
cache: ./cache/cord-024571-vlklgd3x.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206567/
doi: 10.1177/0894439319858679
summary:
  Others are interested in identifying cohesive subgroups because they may indicate a lack of cross-jurisdictional and cross-sectoral collaboration in ERNs. During these responses, public organizations in different jurisdictions participate, and a sizable number of organizations from nongovernmental sectors also become involved (Celik & Corbacioglu, 2016; Comfort & Haase, 2006; Kapucu et al., 2010; Spiro, Acton, & Butts, 2013) . In August 2016, Hanyang university''s research center in South Korea provided an online tagging tool for every news article in the country''s news articles database that included the term "MERS (http://naver.com)." A group of researchers at the Korea Institute for Health and Social Affairs wrote the white paper (488 pages, plus appendices) based on their comprehensive research using multiple data sources and collection methods. These communities included organizations across government jurisdictions, sectors, and geographic locations ( Table 2 , description) and were actively involved in the response during the MERS outbreak.

abstract:
  This article distinguishes between clique family subgroups and communities in a crisis response network. Then, we examine the way organizations interacted to achieve a common goal by employing community analysis of an epidemic response network in Korea in 2015. The results indicate that the network split into two groups: core response communities in one group and supportive functional communities in the other. The core response communities include organizations across government jurisdictions, sectors, and geographic locations. Other communities are confined geographically, homogenous functionally, or both. We also find that whenever intergovernmental relations were present in communities, the member connectivity was low, even if intersectoral relations appeared together within them.


id: cord-024742-hc443akd
title: Epidemic spreading on time-varying multiplex networks
author: Liu, Quan-Hui
date: 2018-12-03
txt: ./txt/cord-024742-hc443akd.txt
cache: ./cache/cord-024742-hc443akd.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219435/
doi: 10.1103/physreve.98.062303
summary:
  We found that higher values of multiplexity significantly reduce the epidemic threshold especially when the temporal activation patterns of nodes present on multiple layers are positively correlated. In such a scenario the epidemic threshold is not affected by the multiplexity, its value is equivalent to the case of a monoplex, and the coupling affects only the layer featuring the smaller average connectivity. In particular, the study of a wide range of real systems shows a complex and case dependent phenomenology in which the topological features (i.e., static connectivity patterns) of coupling nodes can be either positively or negatively correlated [9] . To account for such observations and explore their effects on spreading processes, we consider three simple prototypical cases in which the activities of coupling nodes in the two layers are (i) uncorrelated, or (ii) positively and (iii) negatively correlated.

abstract:
  Social interactions are stratified in multiple contexts and are subject to complex temporal dynamics. The systematic study of these two features of social systems has started only very recently, mainly thanks to the development of multiplex and time-varying networks. However, these two advancements have progressed almost in parallel with very little overlap. Thus, the interplay between multiplexity and the temporal nature of connectivity patterns is poorly understood. Here, we aim to tackle this limitation by introducing a time-varying model of multiplex networks. We are interested in characterizing how these two properties affect contagion processes. To this end, we study susceptible-infected-susceptible epidemic models unfolding at comparable timescale with respect to the evolution of the multiplex network. We study both analytically and numerically the epidemic threshold as a function of the multiplexity and the features of each layer. We found that higher values of multiplexity significantly reduce the epidemic threshold especially when the temporal activation patterns of nodes present on multiple layers are positively correlated. Furthermore, when the average connectivity across layers is very different, the contagion dynamics is driven by the features of the more densely connected layer. Here, the epidemic threshold is equivalent to that of a single layered graph and the impact of the disease, in the layer driving the contagion, is independent of the multiplexity. However, this is not the case in the other layers where the spreading dynamics is sharply influenced by it. The results presented provide another step towards the characterization of the properties of real networks and their effects on contagion phenomena.


id: cord-024830-cql4t0r5
author: McMillin, Stephen Edward
date: 2020-05-08
txt: ./txt/cord-024830-cql4t0r5.txt
cache: ./cache/cord-024830-cql4t0r5.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7223672/
doi: 10.1007/s42972-020-00004-z
summary:
  Following Mosley''s (2013) recommendation, this paper examines in detail how a heavily advocated quality improvement pilot program for a maternal and child health network working in a large Midwestern metropolitan area attempted to make mid-implementation course corrections for a universal screening and referral program for perinatal mood and anxiety disorders conducted by its member agencies. By the middle of the program year, network meeting participants explicitly recognized that mid-course corrections were needed in the implementation of the new quality improvement and data-sharing program for universal screening and referral of perinatal mood and anxiety disorders. Regarding the second research question, concerning how advocacy targets needed to change based on the identification of the problem, participants agreed that the previous plan to reinforce the importance of the screening program to senior executives in current and potential partner agencies (McMillin 2017) needed to be updated to reflect a much tighter focus on the line staff actually doing the work (or alternatively not doing the work in the ways expected) in the months remaining in the funded program year.

title:
  Quality Improvement Innovation in a Maternal and Child Health Network: Negotiating Course Corrections in Mid-Implementation

abstract:
  This article analyzes mid-implementation course corrections in a quality improvement innovation for a maternal and child health network working in a large Midwestern metropolitan area. Participating organizations received restrictive funding from this network to screen pregnant women and new mothers for depression, make appropriate referrals, and log screening and referral data into a project-wide data system over a one-year pilot program. This paper asked three research questions: (1) What problems emerged by mid-implementation of this program that required course correction? (2) How were advocacy targets developed to influence network and agency responses to these mid-course problems? (3) What specific course corrections were identified and implemented to get implementation back on track? This ethnographic case study employs qualitative methods including participant observation and interviews. Data were analyzed using the analytic method of qualitative description, in which the goal of data analysis is to summarize and report an event using the ordinary, everyday terms for that event and the unique descriptions of those present. Three key findings are noted. First, network participants quickly responded to the emerged problem of under-performing screening and referral completion statistics. Second, they shifted advocacy targets away from executive appeals and toward the line staff actually providing screening. Third, participants endorsed two specific course corrections, using “opt out, not opt in” choice architecture at intake and implementing visual incentives for workers to track progress. Opt-out choice architecture and visual incentives served as useful means of focusing organizational collaboration and correcting mid-implementation problems.


id: cord-025838-ed6itb9u
title: SIoTPredict: A Framework for Predicting Relationships in the Social Internet of Things
author: Aljubairy, Abdulwahab
date: 2020-05-09
txt: ./txt/cord-025838-ed6itb9u.txt
cache: ./cache/cord-025838-ed6itb9u.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266455/
doi: 10.1007/978-3-030-49435-3_7
summary:
  Specifically, we propose a framework, namely SIoTPredict, which includes three stages: i) collection of raw movement data of IoT devices, ii) generating temporal sequence networks of the SIoT, and iii) predicting relationships among IoT devices which are likely to occur. Therefore, this paper focuses on modelling the SIoT network and study, in particular, the problem of predicting future relationships among IoT objects. In our work, we develop the SIoTPredict framework, which includes three stages: i) collecting the raw movement data of IoT devices, ii) generating temporal sequence networks of SIoT, and iii) predicting future relationships that may be established among things. The SIoTPredict framework consists of three main stages for i) collecting raw movement data of IoT devices, ii) generating temporal sequence networks, and iii) predicting future relationships among things. The framework includes three stages namely: Stage 1: collection of the raw movement data of IoT devices, Stage 2: generating the temporal sequence networks of SIoT, and Stage 3: prediction future relationships of the SIoT.

abstract:
  The Social Internet of Things (SIoT) is a new paradigm that integrates social network concepts with the Internet of Things (IoT). It boosts the discovery, selection and composition of services and information provided by distributed objects. In SIoT, searching for services is based on the utilization of the social structure resulted from the formed relationships. However, current approaches lack modelling and effective analysis of SIoT. In this work, we address this problem and specifically focus on modelling the SIoT’s evolvement. As the growing number of IoT objects with heterogeneous attributes join the social network, there is an urgent need for identifying the mechanisms by which SIoT structures evolve. We model the SIoT over time and address the suitability of traditional analytical procedures to predict future relationships (links) in the dynamic and heterogeneous SIoT. Specifically, we propose a framework, namely SIoTPredict, which includes three stages: i) collection of raw movement data of IoT devices, ii) generating temporal sequence networks of the SIoT, and iii) predicting relationships among IoT devices which are likely to occur. We have conducted extensive experimental studies to evaluate the proposed framework using real SIoT datasets and the results show the better performance of our framework.


id: cord-027286-mckqp89v
author: Ksieniewicz, Paweł
date: 2020-05-23
txt: ./txt/cord-027286-mckqp89v.txt
cache: ./cache/cord-027286-mckqp89v.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303703/
doi: 10.1007/978-3-030-50423-6_16
summary:
  We make use of pattern recognition models to aid optimization of dynamic mcf-based ss-fons in order to improve performance of the network in terms of minimizing bandwidth blocking probability (bbp), or in other words to maximize the amount of traffic that can be allocated in the network. In particular, an important topic in the considered optimization problem is selection of a modulation format (mf) for a particular demand, due to the fact that each mf provides a different tradeoff between required spectrum width and transmission distance. The main novelty and contribution of the following work is an in-depth analysis of the basic regression methods stabilized by the structure of the estimator ensemble [16] and assessment of their usefulness in the task of predicting the objective function for optimization purposes.

title:
  Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks

abstract:
  The following paper considers pattern recognition-aided optimization of complex and relevant problem related to optical networks. For that problem, we propose a four-step dedicated optimization approach that makes use, among others, of a regression method. The main focus of that study is put on the construction of efficient regression model and its application for the initial optimization problem. We therefore perform extensive experiments using realistic network assumptions and then draw conclusions regarding efficient approach configuration. According to the results, the approach performs best using multi-layer perceptron regressor, whose prediction ability was the highest among all tested methods.


id: cord-027304-a0vva8kb
author: Achermann, Guillem
date: 2020-05-23
txt: ./txt/cord-027304-a0vva8kb.txt
cache: ./cache/cord-027304-a0vva8kb.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303723/
doi: 10.1007/978-3-030-50423-6_19
summary:
  By modelling, on one hand, cognitive distance as noise, and, on the other hand, the inefficiencies linked to a bad flow of information as costs, we propose a model of the dynamics by which a horizontal network evolves into a hierarchical network, with some members emerging as intermediaries in the transfer of knowledge between seekers and problem-solvers. Our contribution to the theoretical understanding on the self-organising properties of innovation systems is that, by framing the problem of heterogeneous cognitive distance between organisations under the theory of dissipative systems, we can explain in thermodynamically efficient terms the reduction in entropy of an innovation system, as an emergent adaptation aimed at reducing costs of maintenance of the system''s structure.

title:
  An Information-Theoretic and Dissipative Systems Approach to the Study of Knowledge Diffusion and Emerging Complexity in Innovation Systems

abstract:
  The paper applies information theory and the theory of dissipative systems to discuss the emergence of complexity in an innovation system, as a result of its adaptation to an uneven distribution of the cognitive distance between its members. By modelling, on one hand, cognitive distance as noise, and, on the other hand, the inefficiencies linked to a bad flow of information as costs, we propose a model of the dynamics by which a horizontal network evolves into a hierarchical network, with some members emerging as intermediaries in the transfer of knowledge between seekers and problem-solvers. Our theoretical model contributes to the understanding of the evolution of an innovation system by explaining how the increased complexity of the system can be thermodynamically justified by purely internal factors. Complementing previous studies, we demonstrate mathematically that the complexity of an innovation system can increase not only to address the complexity of the problems that the system has to solve, but also to improve the performance of the system in transferring the knowledge needed to find a solution.


id: cord-027463-uc0j3fyi
title: A New Multilayer Network Construction via Tensor Learning
author: Brandi, Giuseppe
date: 2020-05-25
txt: ./txt/cord-027463-uc0j3fyi.txt
cache: ./cache/cord-027463-uc0j3fyi.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304789/
doi: 10.1007/978-3-030-50433-5_12
summary:
  Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network directly from data. The constructed multilayer network shows a strong interconnection between the volumes and prices layers across all the stocks considered while a lower number of interconnections between the uncertainty measures is identified. In particular, we use the tensor learning approach establish in [6] to estimate the tensor coefficients, which are the building blocks of the multilayer network of the intra and inter dependencies in the analyzed financial data. The multilayer network built via the estimated tensor autoregression coefficient B represents the interconnections between and within each layer. In this paper, we proposed a methodology to build a multilayer network via the estimated coefficient of the Tucker tensor autoregression of [6] .

abstract:
  Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems. The construction of these networks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network directly from data. This methodology captures within and between connections across layers and makes use of a filtering procedure to extract relevant information and improve visualization. We show the application of this methodology to different stationary fractionally differenced financial data. We argue that our result is useful to understand the dependencies across three different aspects of financial risk, namely market risk, liquidity risk, and volatility risk. Indeed, we show how the resulting visualization is a useful tool for risk managers depicting dependency asymmetries between different risk factors and accounting for delayed cross dependencies. The constructed multilayer network shows a strong interconnection between the volumes and prices layers across all the stocks considered while a lower number of interconnections between the uncertainty measures is identified.


id: cord-027719-98tjnry7
title: Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure
author: Said, Abd Mlak
date: 2020-05-31
txt: ./txt/cord-027719-98tjnry7.txt
cache: ./cache/cord-027719-98tjnry7.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313290/
doi: 10.1007/978-3-030-51517-1_3
summary:
  In this paper, we propose an anomaly based rank attack detection system against an IoT network using Support Vector Machines. With the enormous number of devices that are now connected to the Internet, a new solution was proposed: 6LowPan a lightweight protocol that defines how to run IP version 6 (IPv6) over low data rate, low power, small footprint radio networks as typified by the IEEE 802.15.4 radio [11] . As shown in Fig. 3 , an attacker may insert a malicious mote into the network to attract other nodes to establish routes through it by advertising false ranks while the reformulation of the DODAG is done [14] . We implement the centralized anomaly based IDS at the root mote or the sink and we collect and analyze network data as shown in Table 1 summarizes the used simulation parameters. In this paper, we propose an intrusion detection system "IDS" for smart hospital infrastructure data protection.

abstract:
  In recent years, many technologies were racing to deliver the best service for human being. Emerging Internet of Things (IoT) technologies made birth to the notion of smart infrastructures such as smart grid, smart factories or smart hospitals. These infrastructures rely on interconnected smart devices collecting real-time data in order to improve existing procedures and systems capabilities. A critical issue in smart infrastructures is the information protection which may be more valuable than physical assets. Therefore, it is extremely important to detect and deter any attacks or breath to the network system for information theft. One of these attacks is the rank attack that is carried out by an intruder node in order to attract legitimate traffic to it, then steal personal data of different persons (both patients and staffs in hospitals). In this paper, we propose an anomaly based rank attack detection system against an IoT network using Support Vector Machines. As a use case, we are interested in the healthcare sector and in particular in smart hospitals which are multifaceted with many challenges such as service resilience, assets interoperability and sensitive information protection. The proposed intrusion detection system (IDS) is implemented and evaluated using Conticki Cooja simulator. Results show a high detection accuracy and low false positive rates.


id: cord-027851-95bsoea2
author: Wang, Daojuan
date: 2020-06-25
txt: ./txt/cord-027851-95bsoea2.txt
cache: ./cache/cord-027851-95bsoea2.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315112/
doi: 10.1007/s11365-020-00681-y
summary:
  Particularly, some critical contacts in the public sphere, such as venture capitalists, successful entrepreneurs, and business incubators, not only directly bring the nascent entrepreneur valuable suggestions, creative ideas, and financial resources simultaneously, but also play the role of business referrals and endorsements and further broaden the entrepreneur''s opportunities for acquiring and enhancing innovation and financing capabilities (Van Osnabrugge and Robinson 2000; Mason and Stark 2004; Löfsten and Lindelöf 2005; Cooper and Park 2008; Ramos-Rodríguez et al. An entrepreneur''s networking with a potential investor was also found to benefit the coupling between financing and innovation in the startup, as expected. The literal meaning of ''entrepreneur'' is going in between and taking a benefit, and in our study the entrepreneur is going between an investor and a researcher, and combining advice or investment from the former with advice or new idea from the latter, and thereby promotes a coupling of financing and innovation, a synergy that builds a capability and a competitive advantage.

title:
  Coupling between financing and innovation in a startup: embedded in networks with investors and researchers

abstract:
  Innovation may be a basis for starting a business, and financing is typically needed for starting. Innovation and financing may conceivably be negatively related, or be unrelated, or plausibly be beneficially related. These possible scenarios frame the questions: What is the coupling between innovation and financing at inception, and what is the embeddedness of coupling in networks around the entrepreneur, specifically networks with investors and researchers? These questions are addressed with a globally representative sample of entrepreneurs interviewed at inception of their business. Innovation and financing are found to be decoupled, typically; less frequently to be loosely coupled, and rarely to be tightly coupled. Coupling is promoted by networking with both investors and researchers, with additive effects and with a synergy effect. By ascertaining coupling and its embeddedness in networks as a way for building capability in a startup, the study contributes to empirically supported theorizing about capability building.


id: cord-028685-b1eju2z7
title: Rough Net Approach for Community Detection Analysis in Complex Networks
author: Fuentes, Ivett
date: 2020-06-10
txt: ./txt/cord-028685-b1eju2z7.txt
cache: ./cache/cord-028685-b1eju2z7.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338191/
doi: 10.1007/978-3-030-52705-1_30
summary:
  Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. In this section, we describe the application of Rough Net in important tasks of the CD analysis: the validation and visualization of detected communities and their interactions, and the evolutionary estimation in dynamic networks. Thus, we propose a new approach for visualizing the interactions between communities taking into account the quality of the community structure by using the combination of the Rough Net definition and the complex network representation. For illustrating the performance of the Rough Net definition in the community detection analysis, we apply it to three networks, two known to have monoplex topology and the third multiplex one. In this paper, we have described new quality measures for exploratory analysis of community structure in both monoplex and multiplex networks based on the Rough Net definition.

abstract:
  Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.


id: cord-028688-5uzl1jpu
title: Multi-granularity Complex Network Representation Learning
author: Li, Peisen
date: 2020-06-10
txt: ./txt/cord-028688-5uzl1jpu.txt
cache: ./cache/cord-028688-5uzl1jpu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338194/
doi: 10.1007/978-3-030-52705-1_18
summary:
  In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. A series of deep learning-based network representation methods were then proposed to further solve the problems of global topological structure preservation and high-order nonlinearity of data, and increased efficiency. So these location attributes and activity information are inherently indecomposable and interdependence with the suspect, making the two nodes recognize at a finer granularity based on the additional information and relationship structure that the low-dimensional representation vectors learned have certain similarities. To better characterize multiple granularity complex networks and solve the problem of nodes with potential associations that cannot be processed through the relationship structure alone, we refine the granularity to additional attributes, and designed an information fusion method, which are defined as follows:

abstract:
  Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.


id: cord-029277-mjpwkm2u
title: An Abstraction-Based Framework for Neural Network Verification
author: Elboher, Yizhak Yisrael
date: 2020-06-13
txt: ./txt/cord-029277-mjpwkm2u.txt
cache: ./cache/cord-029277-mjpwkm2u.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363186/
doi: 10.1007/978-3-030-53288-8_3
summary:
  Different verification approaches may differ in (i) the kinds of neural networks they allow (specifically, the kinds of activation functions in use); (ii) the kinds of input properties; and (iii) the kinds of output properties. Because the complexity of verifying a neural network is strongly connected to its size [20] , our goal is to transform a verification query ϕ 1 = N, P, Q into query ϕ 2 = N , P, Q , such that the abstract networkN is significantly smaller than N (notice that properties P and Q remain unchanged). Together with a black-box verification procedure Verify that can dispatch queries of the form ϕ = N, P, Q , these components now allow us to design an abstraction-refinement algorithm for DNN verification, given as Algorithm 1 (we assume that all hidden neurons in the input network have already been marked pos/neg and inc/dec).

abstract:
  Deep neural networks are increasingly being used as controllers for safety-critical systems. Because neural networks are opaque, certifying their correctness is a significant challenge. To address this issue, several neural network verification approaches have recently been proposed. However, these approaches afford limited scalability, and applying them to large networks can be challenging. In this paper, we propose a framework that can enhance neural network verification techniques by using over-approximation to reduce the size of the network—thus making it more amenable to verification. We perform the approximation such that if the property holds for the smaller (abstract) network, it holds for the original as well. The over-approximation may be too coarse, in which case the underlying verification tool might return a spurious counterexample. Under such conditions, we perform counterexample-guided refinement to adjust the approximation, and then repeat the process. Our approach is orthogonal to, and can be integrated with, many existing verification techniques. For evaluation purposes, we integrate it with the recently proposed Marabou framework, and observe a significant improvement in Marabou’s performance. Our experiments demonstrate the great potential of our approach for verifying larger neural networks.


id: cord-031663-i71w0es7
title: How Many Bits Does it Take to Quantize Your Neural Network?
author: Giacobbe, Mirco
date: 2020-03-13
txt: ./txt/cord-031663-i71w0es7.txt
cache: ./cache/cord-031663-i71w0es7.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480702/
doi: 10.1007/978-3-030-45237-7_5
summary:
  For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. As a result, we obtain a encoding into a first-order logic formula which, in contrast to a standard unbalanced linear encoding, makes the verification of quantized networks practical and amenable to modern bit-precise SMT-solving. We measured the robustness to attacks of a neural classifier involving 890 neurons and trained on the MNIST dataset (handwritten digits), for quantizations between 6 and 10 bits. We evaluated whether our balanced encoding strategy, compared to a standard linear encoding, can improve the scalability of contemporary SMT solvers for quantifier-free bit-vectors (QF BV) to check specifications of quantized neural networks. We introduced the first complete method for the verification of quantized neural networks which, by SMT solving over bit-vectors, accounts for their bit-precise semantics.

abstract:
  Quantization converts neural networks into low-bit fixed-point computations which can be carried out by efficient integer-only hardware, and is standard practice for the deployment of neural networks on real-time embedded devices. However, like their real-numbered counterpart, quantized networks are not immune to malicious misclassification caused by adversarial attacks. We investigate how quantization affects a network’s robustness to adversarial attacks, which is a formal verification question. We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. We built a tool and analyzed the effect of quantization on a classifier for the MNIST dataset. We demonstrate that, compared to our method, existing methods for the analysis of real-numbered networks often derive false conclusions about their quantizations, both when determining robustness and when detecting attacks, and that existing methods for quantized networks often miss attacks. Furthermore, we applied our method beyond robustness, showing how the number of bits in quantization enlarges the gender bias of a predictor for students’ grades.


id: cord-033557-fhenhjvm
author: Saha, Debdatta
date: 2020-10-09
txt: ./txt/cord-033557-fhenhjvm.txt
cache: ./cache/cord-033557-fhenhjvm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545157/
doi: 10.1007/s13596-020-00515-w
summary:
  However, the continuity in content of these knowledge systems, which are studied using modern publication standards prescribed by academic journals, indicate a kind of adaptive innovation that we track using an author-affiliation based measure of homophily. The simultaneous existence of research papers from both disciplines for journals conforming to uniform standards of publication automatically raises questions about the true nature of innovation in traditional knowledge systems like Ayurveda. 3 Higher per-paper homophily ( H j ) in achieving higher quality publications; the value of the average SCImago Journal Rank (SJR) is significantly higher at 0.97 for the Ashwagandha network compared to 0.76 for the Amla network. In the specific context of herb-specific academic paper networks in Ayurveda, we find that a lower affiliation-based homophily is causally linked with higher publication ranking, as measured by the SCImago ranks of journals publishing these papers.

title:
  Reconciling conflicting themes of traditionality and innovation: an application of research networks using author affiliation

abstract:
  Innovation takes different forms: varying from path-breaking discoveries to adaptive changes that survive external shifts in the environment. Our paper investigates the nature and process of innovation in the traditional knowledge system of Ayurveda by tracing the footprints that innovation leaves in the academic research network of published papers from the PubMed database. Traditional knowledge systems defy the application of standard measures of innovation such as patents and patent citations. However, the continuity in content of these knowledge systems, which are studied using modern publication standards prescribed by academic journals, indicate a kind of adaptive innovation that we track using an author-affiliation based measure of homophily. Our investigation of this measure and its relationship with currently accepted standards of journal quality clearly shows how systems of knowledge can continue in an unbroken tradition without becoming extinct. Rather than no innovation, traditional knowledge systems evolve by adapting to modern standards of knowledge dissemination without significant alteration in their content.


id: cord-034824-eelqmzdx
title: Influential Nodes Identification in Complex Networks via Information Entropy
author: Guo, Chungu
date: 2020-02-21
txt: ./txt/cord-034824-eelqmzdx.txt
cache: ./cache/cord-034824-eelqmzdx.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516697/
doi: 10.3390/e22020242
summary:
  In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention. defined the problem of identifying a set of influential spreaders in complex networks as influence maximization problem [57] , and they used hill-climbing based greedy algorithm that is within 63% of optimal in several models. Besides, to make the algorithm practically more useful, we provide EnRenew''s source code and all the experiments details on https://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy, and researchers can download it freely for their convenience.

abstract:
  Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.


id: cord-034833-ynti5g8j
title: Scaling in Colloidal and Biological Networks
author: Nosonovsky, Michael
date: 2020-06-04
txt: ./txt/cord-034833-ynti5g8j.txt
cache: ./cache/cord-034833-ynti5g8j.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517159/
doi: 10.3390/e22060622
summary:
  Scaling relationships in complex networks of neurons, which are organized in the neocortex in a hierarchical manner, suggest that the characteristic time constant is independent of brain size when interspecies comparison is conducted. In this section, we will review certain aspects of the current knowledge about the cortical networks in human and animal brains related to their scaling and self-organizing properties. Several neuroscientists suggested in the 2000s that the human brain network is both scale-free and small-world, although the arguments and evidence for these hypotheses are indirect [42, 53] , including power-law distributions of anatomical connectivity as well as the statistical properties of state transitions in the brain [54] . The brain networks possess many characteristics typical to other networks, including the one-over-frequency and power-law activities, avalanches, small-world, scale-free, and fractal topography.

abstract:
  Scaling and dimensional analysis is applied to networks that describe various physical systems. Some of these networks possess fractal, scale-free, and small-world properties. The amount of information contained in a network is found by calculating its Shannon entropy. First, we consider networks arising from granular and colloidal systems (small colloidal and droplet clusters) due to pairwise interaction between the particles. Many networks found in colloidal science possess self-organizing properties due to the effect of percolation and/or self-organized criticality. Then, we discuss the allometric laws in branching vascular networks, artificial neural networks, cortical neural networks, as well as immune networks, which serve as a source of inspiration for both surface engineering and information technology. Scaling relationships in complex networks of neurons, which are organized in the neocortex in a hierarchical manner, suggest that the characteristic time constant is independent of brain size when interspecies comparison is conducted. The information content, scaling, dimensional, and topological properties of these networks are discussed.


id: cord-048461-397hp1yt
title: Epigrass: a tool to study disease spread in complex networks
author: Coelho, Flávio C
date: 2008-02-26
txt: ./txt/cord-048461-397hp1yt.txt
cache: ./cache/cord-048461-397hp1yt.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275240/
doi: 10.1186/1751-0473-3-3
summary:
  BACKGROUND: The construction of complex spatial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. RESULTS: A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. The Epigrass system is driven by a graphical user interface(GUI), which handles several input files required for model definition and manages the simulation and output generation (figure 2). To run a network epidemic model in Epigrass, the user is required to provide three separate text files (Optionally, also a shapefile with the map layer):

abstract:
  BACKGROUND: The construction of complex spatial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. Such data, which frequently resides on large geo-referenced databases, has to be processed and assigned to the various components of the model. All this just to construct the model, then it still has to be simulated and analyzed under different epidemiological scenarios. This workflow can only be achieved efficiently by computational tools that can automate most, if not all, these time-consuming tasks. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior. RESULTS: A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. Results show that the topological context of the starting point of the epidemic is of great importance from both control and preventive perspectives. CONCLUSION: Epigrass is shown to facilitate greatly the construction, simulation and analysis of complex network models. The output of model results in standard GIS file formats facilitate the post-processing and analysis of results by means of sophisticated GIS software.


id: cord-102776-2upbx2lp
title: Visual analytics for networked-guarantee loans risk management
author: Niu, Zhibin
date: 2017-04-06
url: https://arxiv.org/pdf/1705.02937v2.pdf
doi: 10.1109/pacificvis.2018.00028
abstract:
  Groups of enterprises guarantee each other and form complex guarantee networks when they try to obtain loans from banks. Such secured loan can enhance the solvency and promote the rapid growth in the economic upturn period. However, potential systemic risk may happen within the risk binding community. Especially, during the economic down period, the crisis may spread in the guarantee network like a domino. Monitoring the financial status, preventing or reducing systematic risk when crisis happens is highly concerned by the regulatory commission and banks. We propose visual analytics approach for loan guarantee network risk management, and consolidate the five analysis tasks with financial experts: i) visual analytics for enterprises default risk, whereby a hybrid representation is devised to predict the default risk and developed an interface to visualize key indicators; ii) visual analytics for high default groups, whereby a community detection based interactive approach is presented; iii) visual analytics for high defaults pattern, whereby a motif detection based interactive approach is described, and we adopt a Shneiderman Mantra strategy to reduce the computation complexity. iv) visual analytics for evolving guarantee network, whereby animation is used to help understanding the guarantee dynamic; v) visual analytics approach and interface for default diffusion path. The temporal diffusion path analysis can be useful for the government and bank to monitor the default spread status. It also provides insight for taking precautionary measures to prevent and dissolve systemic financial risk. We implement the system with case studies on a real-world guarantee network. Two financial experts are consulted with endorsement on the developed tool. To the best of our knowledge, this is the first visual analytics tool to explore the guarantee network risks in a systematic manner.


id: cord-103150-e9q8e62v
author: Mishra, Shreya
date: 2020-11-04
txt: ./txt/cord-103150-e9q8e62v.txt
cache: ./cache/cord-103150-e9q8e62v.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.07.24.219196
doi: 10.1101/2020.07.24.219196
summary:
  Just like gene-expression profile, inferred gene network could also be used to find differences in two groups of cells(sample) [13] to reveal changes in the regulatory pattern caused due to disease, environmental exposure or ageing. In order to test the hypothesis that graph-based denoising could improve gene-network inference, we first evaluated the performance of our method on bulk expression data-set. Our approach of graph-wavelet based pre-processing of mESC scRNA-seq data-set improved the performance of gene-network inference methods by 8-10 percentage (Fig. 2B) . Similarly in comparison to graph-wavelet based denoising, the other 7 methods did not provided substantial improvement in AUC for overlap among gene-network inferred by two data-sets of mESC (Fig. 2C , supplementary Figure S1B ). However, graph wavelet-based filtering improved the overlap between networks inferred from different batches of scRNA-seq profile of mESC even if they were denoised separately (Fig. 2C , supplementary Figure S1B ).

title:
  Improving gene-network inference with graph-wavelets and making insights about ageing associated regulatory changes in lungs

abstract:
  Using gene-regulatory-networks based approach for single-cell expression profiles can reveal un-precedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here we devise a conceptually different method using graph-wavelet filters for improving gene-network (GWNet) based analysis of the transcriptome. Our approach improved the performance of several gene-network inference methods. Most Importantly, GWNet improved consistency in the prediction of generegulatory-network using single-cell transcriptome even in presence of batch effect. Consistency of predicted gene-network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically-relevant changes in the influence of pathways and master-regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to effect of novel coronavirus infection in Human Lung.


id: cord-103418-deogedac
title: Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks
author: Ochab, J. K.
date: 2010-11-12
txt: ./txt/cord-103418-deogedac.txt
cache: ./cache/cord-103418-deogedac.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/1011.2985v1.pdf
doi: 10.1140/epjb/e2011-10975-6
summary:
  title: Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks The network was constructed as a 2-dimensional Watts-Strogatz model (500x500 square lattice with additional shortcuts), and the dynamics involved rewiring shortcuts in every time step of the epidemic spread. For both dynamic and static small-world we observe suppression of the average epidemic size dependence on network size in comparison with finite-size scaling known for regular lattice. Nonetheless, qualitatively the epidemic on dynamic small world behaves in the same way as on the static one for the given range of parameters (φ = 0.5 corresponds to every node in the network having on average two additional links). We have shown that introducing dynamics of the long-range links in a smallworld network significantly lowers an epidemic threshold in terms of probability of disease transmission, although the overall dependence on number of shortcuts stays the same.

abstract:
  The aim of the study was to compare the epidemic spread on static and dynamic small-world networks. The network was constructed as a 2-dimensional Watts-Strogatz model (500x500 square lattice with additional shortcuts), and the dynamics involved rewiring shortcuts in every time step of the epidemic spread. The model of the epidemic is SIR with latency time of 3 time steps. The behaviour of the epidemic was checked over the range of shortcut probability per underlying bond 0-0.5. The quantity of interest was percolation threshold for the epidemic spread, for which numerical results were checked against an approximate analytical model. We find a significant lowering of percolation thresholds for the dynamic network in the parameter range given. The result shows that the behaviour of the epidemic on dynamic network is that of a static small world with the number of shortcuts increased by 20.7 +/- 1.4%, while the overall qualitative behaviour stays the same. We derive corrections to the analytical model which account for the effect. For both dynamic and static small-world we observe suppression of the average epidemic size dependence on network size in comparison with finite-size scaling known for regular lattice. We also study the effect of dynamics for several rewiring rates relative to latency time of the disease.


id: cord-104001-5clslvqb
author: Wang, Xiaoqi
date: 2020-10-21
txt: ./txt/cord-104001-5clslvqb.txt
cache: ./cache/cord-104001-5clslvqb.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.10.20.347153
doi: 10.1101/2020.10.20.347153
summary:
  The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. First, a meta path detection self-supervised learning mechanism is developed to train a deep Transformer encoder for learning low-dimensional representations that capture the path-level information on HBNs. Meanwhile, sel-fRL integrates the vertex entity mask task to learn local association of vertices in HBNs. Finally, the representations from the entity mask and meta path detection is concatenated for generating the embedding vectors of nodes in HBNs. The results of link prediction on six datasets show that the proposed selfRL is superior to 25 state-of-the-art methods. • We proposed a two-level self-supervised representation learning method for HBNs, where this study integrates the meta path detection and vertex entity mask selfsupervised learning task based on a great number of unlabeled data to learn high quality representation vector of vertices.

title:
  selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks

abstract:
  Predicting potential links in heterogeneous biomedical networks (HBNs) can greatly benefit various important biomedical problem. However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. Finally, the representations from two tasks are concatenated to generate high-quality representation vectors. The results of link prediction on six datasets show selfRL outperforms 25 state-of-the-art methods. In particular, selfRL reveals great performance with results close to 1 in terms of AUC and AUPR on the NeoDTI-net dataset. In addition, the PubMed publications demonstrate that nine out of ten drugs screened by selfRL can inhibit the cytokine storm in COVID-19 patients. In summary, selfRL provides a general frame-work that develops self-supervised learning tasks with unlabeled data to obtain promising representations for improving link prediction.


id: cord-125979-2c2agvex
author: Mata, Ang''elica S.
date: 2020-10-05
txt: ./txt/cord-125979-2c2agvex.txt
cache: ./cache/cord-125979-2c2agvex.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.02360v1.pdf
doi: nan
summary:
  Both SIS and SIRS models are equivalent from the mean-field theory perspective, but the mechanism of immunization changes the behavior of the epidemic dynamics depending on the heterogeneity of the network structure. For the SIS model, the central issue is to determine an epidemic threshold separating an absorbing, disease-free state from an active phase on heterogeneous networks [10] [11] [12] [13] [14] [15] [16] [17] [18] . The simplest theory of epidemic spreading assumes that the population can be divided into different compartments according to the stage of the disease (for example, susceptible and infected in both SIS and CP models) and within each compartment, individuals (vertices in the complex networks'' jargon) are assumed to be identical and have approximately the same number of neighbors (edges), k ≈ k . For these distributions, the second moment k 2 diverges in the limit of infinite sizes implying a vanishing threshold for the SIS model or, equivalently, the epidemic prevalence for any finite infection rate.

title:
  An overview of epidemic models with phase transitions to absorbing states running on top of complex networks

abstract:
  Dynamical systems running on the top of complex networks has been extensively investigated for decades. But this topic still remains among the most relevant issues in complex network theory due to its range of applicability. The contact process (CP) and the susceptible-infected-susceptible (SIS) model are used quite often to describe epidemic dynamics. Despite their simplicity, these models are robust to predict the kernel of real situations. In this work, we review concisely both processes that are well-known and very applied examples of models that exhibit absorbing-state phase transitions. In the epidemic scenario, individuals can be infected or susceptible. A phase transition between a disease-free (absorbing) state and an active stationary phase (where a fraction of the population is infected) are separated by an epidemic threshold. For the SIS model, the central issue is to determine this epidemic threshold on heterogeneous networks. For the CP model, the main interest is to relate critical exponents with statistical properties of the network.


id: cord-127900-78x19fw4
title: Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies
author: Leung, Abby
date: 2020-10-06
txt: ./txt/cord-127900-78x19fw4.txt
cache: ./cache/cord-127900-78x19fw4.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.03081v1.pdf
doi: nan
summary:
  More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening. Only by incorporating real world contact networks into compartment models, one can disconnect network hubs to realistically simulate the effect of closure. We focus on the effects of 4 widely adopted NPIs: 1) quarantining infected and exposed individuals, 2) social distancing, 3) closing down of non-essential work places and schools, and 4) the use of face masks. • We show that structure of the contact networks significantly changes the epidemic curves and the current compartment based models are subject to overestimating the scale of the spread • We demonstrate the degree of effectiveness of different NPIs depends on the assumed underlying structure of the contact networks

abstract:
  The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening.


id: cord-133273-kvyzuayp
author: Christ, Andreas
date: 2020-10-05
txt: ./txt/cord-133273-kvyzuayp.txt
cache: ./cache/cord-133273-kvyzuayp.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.16241v1.pdf
doi: nan
summary:
  During the literature review it was evident the presence of few works dedicated to evaluating comprehensively the complete cycle of biofeedback, which comprises using the wearable devices, applying Machine Learning patterns detection algorithms, generate the psychologic intervention, besides monitoring its effects and recording the history of events [9, 3] . This solution is being proposed by several literature study about stress patterns and physiological aspects but with few results, for this reason, our project will address topics like experimental study protocol on signals acquisition from patients/participants with wearables to data acquisition and processing, in sequence will be applied machine learning modeling and prediction on biosignal data regarding stress (Fig. 1) . We will present first results of the project concerning a new process model for cooperating data scientists and quality engineers, a product testing model as knowledge base for machine learning computing and visual support of quality engineers in order to explain prediction results.

title:
  Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020)

abstract:
  The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes 'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.


id: cord-134926-dk28wutc
title: Scalable Estimation of Epidemic Thresholds via Node Sampling
author: Dasgupta, Anirban
date: 2020-07-28
txt: ./txt/cord-134926-dk28wutc.txt
cache: ./cache/cord-134926-dk28wutc.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.14820v1.pdf
doi: nan
summary:
  In this paper, we address these gaps by developing a novel sampling-based method to estimate the epidemic threshold under the widely used Chung-Lu model (Aiello et al., 2000) , also known as the configuration model. Furthermore, eigenvalue algorithms typically require the full matrix to be stored in the random-access memory of the computer, which can be infeasible for massive social contact networks which are too large to be stored. However, in the context of epidemic thresholds, we are interested in the random variable λ(A) itself, as we want to study the contagion spread conditional on a given social contact network. In this work, we investigated the problem of computing SIR epidemic thresholds of social contact networks from the perspective of statistical inference. We would like to state that in this work, the question of epidemic threshold estimation has been formalized from a theoretical viewpoint in a much used, but simple, random graph model.

abstract:
  Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today's interconnected global society, such contagion processes can cause global public health hazards, as exemplified by the ongoing Covid-19 pandemic. It is therefore of great practical relevance to investigate the network trans-mission of contagious diseases from the perspective of statistical inference. An important and widely studied boundary condition for contagion processes over networks is the so-called epidemic threshold. The epidemic threshold plays a key role in determining whether a pathogen introduced into a social contact network will cause an epidemic or die out. In this paper, we investigate epidemic thresholds from the perspective of statistical network inference. We identify two major challenges that are caused by high computational and sampling complexity of the epidemic threshold. We develop two statistically accurate and computationally efficient approximation techniques to address these issues under the Chung-Lu modeling framework. The second approximation, which is based on random walk sampling, further enjoys the advantage of requiring data on a vanishingly small fraction of nodes. We establish theoretical guarantees for both methods and demonstrate their empirical superiority.


id: cord-143847-vtwn5mmd
title: ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
author: Ryffel, Th''eo
date: 2020-06-08
txt: ./txt/cord-143847-vtwn5mmd.txt
cache: ./cache/cord-143847-vtwn5mmd.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.04593v1.pdf
doi: nan
summary:
  This framework implements semi-honest 2-party computation and leverages function secret sharing, a recent cryptographic protocol that only uses lightweight primitives to achieve an efficient online phase with a single message of the size of the inputs, for operations like comparison and multiplication which are building blocks of neural networks. Secure multiparty computation (SMPC) is a promising technique that can efficiently be integrated into machine learning workflows to ensure data and model privacy, while allowing multiple parties or institutions to participate in a joint project. • We show how these blocks can be used in machine learning to implement operations for secure evaluation and training of arbitrary models on private data, including MaxPool and BatchNorm. Our major contribution to the function secret sharing scheme is regarding comparison (which allows to tackle non-polynomial activation functions for neural networks): we build on the idea of the equality test to provide a synthetic and efficient protocol whose structure is very close from the previous one.

abstract:
  We propose ARIANN, a low-interaction framework to perform private training and inference of standard deep neural networks on sensitive data. This framework implements semi-honest 2-party computation and leverages function secret sharing, a recent cryptographic protocol that only uses lightweight primitives to achieve an efficient online phase with a single message of the size of the inputs, for operations like comparison and multiplication which are building blocks of neural networks. Built on top of PyTorch, it offers a wide range of functions including ReLU, MaxPool and BatchNorm, and allows to use models like AlexNet or ResNet18. We report experimental results for inference and training over distant servers. Last, we propose an extension to support n-party private federated learning.


id: cord-148358-q30zlgwy
title: An analysis of network filtering methods to sovereign bond yields during COVID-19
author: Pang, Raymond Ka-Kay
date: 2020-09-28
txt: ./txt/cord-148358-q30zlgwy.txt
cache: ./cache/cord-148358-q30zlgwy.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.13390v1.pdf
doi: nan
summary:
  We find that the average correlation between sovereign bonds within the COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods. The advantages in using filtering methods is the extraction of a network type structure from the financial correlations between sovereign bonds, which allows the properties of centrality and clustering to be considered. In consequence, the correlation-based networks and hierarchical clustering methodologies allow us to understand the nature of financial markets and some features of sovereign bonds. We apply in Section 3 the filtering methods to sovereign bond yields and analyze the trend of financial correlations over the last decade and consider aspects of the network topology. In this paper, we consider the movements of European sovereign bond yields for network filtering methods, where we particularly focus on the COVID-19 period.

abstract:
  In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields amongst European countries. We consider the temporal changes from financial correlations using network filtering methods. These methods consider a subset of links within the correlation matrix, which gives rise to a network structure. We use sovereign bond yield data from 17 European countries between the 2010 and 2020 period as an indicator of the economic health of countries. We find that the average correlation between sovereign bonds within the COVID-19 period decreases, from the peak observed in the 2019-2020 period, where this trend is also reflected in all network filtering methods. We also find variations between the movements of different network filtering methods under various network measures.


id: cord-155440-7l8tatwq
title: Online network monitoring
author: Malinovskaya, Anna
date: 2020-10-19
txt: ./txt/cord-155440-7l8tatwq.txt
cache: ./cache/cord-155440-7l8tatwq.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.09398v1.pdf
doi: nan
summary:
  Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks determined by temporal exponential random graph models (TERGM). The leading SPC tool for analysis is a control chart, which exists in various forms in terms of the number of variables, data type and different statistics being of interest. To conduct surveillance over Y t , we propose to consider only the dynamically estimated parameters of a random graph model in order to reduce computational complexity and to allow for real-time monitoring. In this case, as well as fine-tuning the configuration of statistics, one can modify some settings which design the estimation procedure of the model parameter, for example, the run time, the sample size or the step length (Morris et al., 2008) . In this paper, we show how multivariate control charts can be used to detect changes in TERGM networks. Monitoring of social network and change detection by applying statistical process: ERGM

abstract:
  The application of network analysis has found great success in a wide variety of disciplines; however, the popularity of these approaches has revealed the difficulty in handling networks whose complexity scales rapidly. One of the main interests in network analysis is the online detection of anomalous behaviour. To overcome the curse of dimensionality, we introduce a network surveillance method bringing together network modelling and statistical process control. Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks determined by temporal exponential random graph models (TERGM). This allows us to account for potential temporal dependence, while simultaneously reducing the number of parameters to be monitored. The performance of the proposed charts is evaluated by calculating the average run length for both simulated and real data. To prove the appropriateness of the TERGM to describe network data some measures of goodness of fit are inspected. We demonstrate the effectiveness of the proposed approach by an empirical application, monitoring daily flights in the United States to detect anomalous patterns.


id: cord-163462-s4kotii8
title: 6G for Bridging the Digital Divide: Wireless Connectivity to Remote Areas
author: Chaoub, Abdelaali
date: 2020-09-09
txt: ./txt/cord-163462-s4kotii8.txt
cache: ./cache/cord-163462-s4kotii8.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.04175v1.pdf
doi: nan
summary:
  In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions. At the same time, digitalization in remote areas calls for large coverage solutions (e.g., TV or GSM white spaces (WSs)) to increase the number of users within a base station and helps reduce the network deployment and management costs, albeit at some performance trade-offs. The latest developments in wireless communications can be applied in outdoor power line communication (PLC) to provide high data rate connectivity over the high and medium voltages power lines, increasing the capability of the backhaul networks in remote areas. Service accessibility in rural areas involves prohibitive deployment expenditures for network operators and requires high-capacity backhaul connections for several different use cases.

abstract:
  In telecommunications, network sustainability as a requirement is closely related to equitably serving the population residing at locations that can most appropriately be described as remote. The first four generations of mobile communication ignored the remote connectivity requirements, and the fifth generation is addressing it as an afterthought. However, sustainability and its social impact are being positioned as key drivers of sixth generation's (6G) standardization activities. In particular, there has been a conscious attempt to understand the demands of remote wireless connectivity, which has led to a better understanding of the challenges that lie ahead. In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions.


id: cord-164703-lwwd8q3c
title: Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment
author: Noury, Zahra
date: 2020-06-15
txt: ./txt/cord-164703-lwwd8q3c.txt
cache: ./cache/cord-164703-lwwd8q3c.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.08296v2.pdf
doi: nan
summary:
  One of the commonly used practices is using text-based CAPTCHAs. An example of these types of questions can be seen in Figure 2 , in which a sequence of random alphanumeric characters or digits or combinations of them are distorted and drawn in a noisy image. Geetika Garg and Chris Pollett [1] performed a trained Python-based deep neural network to crack fix-lengthed CAPTCHAs. The network consists of two Convolutional Maxpool layers, followed by a dense layer and a Softmax output layer. However, they have used three Convolutional layers followed by two dense layers and then the classifiers to solve six-digit CAPTCHAs. Besides, they have used a technique to reduce the size of the required training dataset. Also, we trained the network on 700,000 alphanumerical CAPTCHAs. For a better comparison and to have a more consistent approach, we only increased the number of neurons in each Softmax units from 10 to 31 to cover all common Latin characters and digits.

abstract:
  CAPTCHA is a human-centred test to distinguish a human operator from bots, attacking programs, or other computerised agents that tries to imitate human intelligence. In this research, we investigate a way to crack visual CAPTCHA tests by an automated deep learning based solution. The goal of this research is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator systems; hence, developing more robust CAPTCHAs, without taking the risks of manual try and fail efforts. We develop a Convolutional Neural Network called Deep-CAPTCHA to achieve this goal. The proposed platform is able to investigate both numerical and alphanumerical CAPTCHAs. To train and develop an efficient model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues. Our network's cracking accuracy leads to a high rate of 98.94% and 98.31% for the numerical and the alpha-numerical test datasets, respectively. That means more works is required to develop robust CAPTCHAs, to be non-crackable against automated artificial agents. As the outcome of this research, we identify some efficient techniques to improve the security of the CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA model.


id: cord-168862-3tj63eve
title: Nonlinearity + Networks: A 2020 Vision
author: Porter, Mason A.
date: 2019-11-09
txt: ./txt/cord-168862-3tj63eve.txt
cache: ./cache/cord-168862-3tj63eve.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/1911.03805v1.pdf
doi: nan
summary:
  However, recent uses of the term "network" have focused increasingly on connectivity patterns that are more general than graphs [98] : a network''s nodes and/or edges (or their associated weights) can change in time [70, 72] (see Section 3), nodes and edges can include annotations [26] , a network can include multiple types of edges and/or multiple types of nodes [90, 140] , it can have associated dynamical processes [142] (see Sections 3, 4, and 5) , it can include memory [152] , connections can occur between an arbitrary number of entities [127, 131] (see Section 6) , and so on. Following a long line of research in sociology [37] , two important ingredients in the study of networks are examining (1) the importances ("centralities") of nodes, edges, and other small network structures and the relationship of measures of importance to dynamical processes on networks and (2) the large-scale organization of networks [121, 193] .

abstract:
  I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include"higher-order"interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.


id: cord-186031-b1f9wtfn
title: Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy
author: Caldarelli, Guido
date: 2020-10-05
txt: ./txt/cord-186031-b1f9wtfn.txt
cache: ./cache/cord-186031-b1f9wtfn.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.01913v1.pdf
doi: nan
summary:
  When analysing the emerging 4 communities, we find that they correspond to 1 Right wing parties and media (in steel blue) 2 Center left wing (dark red) 3 5 Stars Movement (M5S ), in dark orange 4 Institutional accounts (in sky blue) Details about the political situation in Italy during the period of data collection can be found in the Supplementary Material, Section 1.2: ''Italian political situation during the Covid-19 pandemics''. In line with previous results on the validated network of verified users, the table clearly shows how the vast majority of the news coming from sources considered scarce or non reputable are tweeted and retweeted by the center-right and right wing communities; 98% of the domains tagged as NR are shared by them.

abstract:
  During the Covid-19 pandemics, we also experience another dangerous pandemics based on misinformation. Narratives disconnected from fact-checking on the origin and cure of the disease intertwined with pre-existing political fights. We collect a database on Twitter posts and analyse the topology of the networks of retweeters (users broadcasting again the same elementary piece of information, or tweet) and validate its structure with methods of statistical physics of networks. Furthermore, by using commonly available fact checking software, we assess the reputation of the pieces of news exchanged. By using a combination of theoretical and practical weapons, we are able to track down the flow of misinformation in a snapshot of the Twitter ecosystem. Thanks to the presence of verified users, we can also assign a polarization to the network nodes (users) and see the impact of low-quality information producers and spreaders in the Twitter ecosystem.


id: cord-191876-03a757gf
title: Processing of Crowdsourced Observations of Aircraft in a High Performance Computing Environment
author: Weinert, Andrew
date: 2020-08-03
txt: ./txt/cord-191876-03a757gf.txt
cache: ./cache/cord-191876-03a757gf.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.00861v1.pdf
doi: nan
summary:
  We''ve previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. In response, we previously identified and determined that the OpenSky Network [4] , a community network of ground-based sensors that observe aircraft equipped with Automatic Dependent Surveillance-Broadcast (ADS-B) out, would provide sufficient and appropriate data to develop new models [5] . Additionally to address that the four aircraft registries do not contain all registered aircraft globally, a second level directory titled "Unknown" was created and populated with directories corresponding to each hour of data. This hierarchy ensures that there are no more than 1000 directories per level, as recommended by the LLSC, while organizing the data to easily enable comparative analysis between years or different types of aircraft.

abstract:
  As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain.


id: cord-198449-cru40qp4
title: Incorporating social opinion in the evolution of an epidemic spread
author: Carballosa, Alejandro
date: 2020-07-09
txt: ./txt/cord-198449-cru40qp4.txt
cache: ./cache/cord-198449-cru40qp4.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.04619v1.pdf
doi: nan
summary:
  It has been shown that the most effective way to control the virulent spread of a disease is to break down the connectivity of these networks of interactions, by means of imposing social distancing and isolation measures to the population [1] . Again, this approach would depend on the adherence of the population to the confinement policies, and taking into account the rogue individuals that bypass the confinement measures, it is important to accurately characterize the infection curves and the prediction of short-term new cases of the disease, since they can be responsible of a dramatic spread. We established four different scenarios: for the first one we considered a theoretical situation where we imposed that around the 70% of the population will adopt social distancing measures, but leave the other 30% in a situation where they either have an opinion against the policies or they have to move around interacting with the rest of the network for any reason (this means, ̅ = 0.3 for all the nodes).

abstract:
  Attempts to control the epidemic spread of COVID19 in the different countries often involve imposing restrictions to the mobility of citizens. Recent examples demonstrate that the effectiveness of these policies strongly depends on the willingness of the population to adhere them. And this is a parameter that it is difficult to measure and control. We demonstrate in this manuscript a systematic way to check the mood of a society and a way to incorporate it into dynamical models of epidemic propagation. We exemplify the process considering the case of Spain although the results and methodology can be directly extrapolated to other countries.


id: cord-200147-ans8d3oa
title: Neural Networks and Value at Risk
author: Arimond, Alexander
date: 2020-05-04
txt: ./txt/cord-200147-ans8d3oa.txt
cache: ./cache/cord-200147-ans8d3oa.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2005.01686v2.pdf
doi: nan
summary:
  Specifically, we estimate VaR thresholds using classic methods (i.e. Mean/Variance, Hidden Markov Model) 1 as well as machine learning methods (i.e. feed forward, convolutional, recurrent), which we advance via initialization of input parameter and regularization of incentive function. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network''s input parameter, (ii) its incentive function according to which it has been trained and which can lead to extreme outputs if it is not regularized as well as (iii) the amount of data we feed. Whereas our paper is focused on advancing machine learning techniques and is therefore following Billio and Pellizon (2000) anchored in a regime based asset allocation setting 1 to account for time varying economic states (CPZ, 2020), we still believe that the nonlinearity and flexible form especially of recurrent neural networks maybe of interesting to the VaR (forecasting) literature (Billio et al.

abstract:
  Utilizing a generative regime switching framework, we perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network, (ii) its incentive function according to which it has been trained and (iii) the amount of data we feed. First, we compare neural networks with random seeding with networks that are initialized via estimations from the best-established model (i.e. the Hidden Markov). We find latter to outperform in terms of the frequency of VaR breaches (i.e. the realized return falling short of the estimated VaR threshold). Second, we balance the incentive structure of the loss function of our networks by adding a second objective to the training instructions so that the neural networks optimize for accuracy while also aiming to stay in empirically realistic regime distributions (i.e. bull vs. bear market frequencies). In particular this design feature enables the balanced incentive recurrent neural network (RNN) to outperform the single incentive RNN as well as any other neural network or established approach by statistically and economically significant levels. Third, we half our training data set of 2,000 days. We find our networks when fed with substantially less data (i.e. 1,000 days) to perform significantly worse which highlights a crucial weakness of neural networks in their dependence on very large data sets ...


id: cord-200354-t20v00tk
title: Experimental Analysis of Communication Relaying Delay in Low-Energy Ad-hoc Networks
author: Miya, Taichi
date: 2020-10-29
txt: ./txt/cord-200354-t20v00tk.txt
cache: ./cache/cord-200354-t20v00tk.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.15572v1.pdf
doi: nan
summary:
  In recent years, more and more applications use ad-hoc networks for local M2M communications, but in some cases such as when using WSNs, the software processing delay induced by packets relaying may not be negligible. The results demonstrated that, in low-energy ad-hoc networks, processing delay of the application is always too large to ignore; it is at least ten times greater than the kernel routing and corresponds to 30% of the transmission delay. I, the goal of this study is to evaluate the impact of software packet processing, induced by packet relaying, to the end-to-end delay, on the basis of an actual measurement assuming an ad-hoc network consisting of small devices with low-power processors. Furthermore, node delay was greater than link delay when the payload size was over 1200 bytes in Enc. In this work, we have designed and conducted an experiment to measure the software processing delay caused by packets relaying.

abstract:
  In recent years, more and more applications use ad-hoc networks for local M2M communications, but in some cases such as when using WSNs, the software processing delay induced by packets relaying may not be negligible. In this paper, we planned and carried out a delay measurement experiment using Raspberry Pi Zero W. The results demonstrated that, in low-energy ad-hoc networks, processing delay of the application is always too large to ignore; it is at least ten times greater than the kernel routing and corresponds to 30% of the transmission delay. Furthermore, if the task is CPU-intensive, such as packet encryption, the processing delay can be greater than the transmission delay and its behavior is represented by a simple linear model. Our findings indicate that the key factor for achieving QoS in ad-hoc networks is an appropriate node-to-node load balancing that takes into account the CPU performance and the amount of traffic passing through each node.


id: cord-203872-r3vb1m5p
title: Availability of demographic cues can negatively impact creativity in dynamic social networks
author: Baten, Raiyan Abdul
date: 2020-07-12
txt: ./txt/cord-203872-r3vb1m5p.txt
cache: ./cache/cord-203872-r3vb1m5p.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.05937v1.pdf
doi: nan
summary:
  If people form and maintain social links only with peers from particular demographic identities (i.e., homophily-guided network dynamics), then it can result in making their stimuli set uniform as the diversity bonuses will go missing. Therefore, as exogenous features, we choose three attributes that the treatment egos were most likely to consider in making their connectivity decisions: (a) the roundwise creative performances of the alters (measured by non-redundant idea counts; see Materials and Methods), (b) gender-based homophily and (c) race-based homophily. Typical settings in convergent thinking or collective intelligence research explore how people, under various study conditions, can get close to known correct answers in estimation tasks [37, 32, 38, 39, Cosine similarities between the idea-sets of pairs of egos are shown across three sub-groups: ego-pairs who share 0, 1 and 2 common alters between them.

abstract:
  As the world braces itself for a pandemic-induced surge in automation and a consequent (accelerated) shift in the nature of jobs, it is essential now more than ever to understand how people's creative performances are impacted by their interactions with peers in a social network. However, when it comes to creative ideation, it is unclear how the demographic cues of one's peers can influence the network dynamics and the associated performance outcomes of people. In this paper, we ask: (1) Given the task of creative idea generation, how do social network connectivities adapt to people's demographic cues? (2) How are creative outcomes influenced by such demography-informed network dynamics? We find that link formations in creativity-centric networks are primarily guided by the creative performances of one's peers. However, in the presence of demographic information, the odds of same-gender links to persist increase by 82.03%, after controlling for merit-based link persistence. In essence, homophily-guided link persistence takes place when demographic cues are available. We further find that the semantic similarities between socially stimulated idea-sets increase significantly in the presence of demographic cues (P<1e-4), which is counter-productive for the purposes of divergent creativity. This result can partly be explained by the observation that people's ideas tend to be more homogeneous within demographic groups than between demographic groups (P<1e-7). Therefore, choosing to maintain connections based on demographic similarity can negatively impact one's creative inspiration sources by taking away potential diversity bonuses. Our results can inform intelligent intervention possibilities towards maximizing a social system's creative outcomes.


id: cord-206872-t6lr3g1m
title: A Survey of State-of-the-Art on Blockchains: Theories, Modelings, and Tools
author: Huang, Huawei
date: 2020-07-07
url: https://arxiv.org/pdf/2007.03520v1.pdf
doi: nan
abstract:
  To draw a roadmap of current research activities of the blockchain community, we first conduct a brief overview of state-of-the-art blockchain surveys published in the recent 5 years. We found that those surveys are basically studying the blockchain-based applications, such as blockchain-assisted Internet of Things (IoT), business applications, security-enabled solutions, and many other applications in diverse fields. However, we think that a comprehensive survey towards the essentials of blockchains by exploiting the state-of-the-art theoretical modelings, analytic models, and useful experiment tools is still missing. To fill this gap, we perform a thorough survey by identifying and classifying the most recent high-quality research outputs that are closely related to the theoretical findings and essential mechanisms of blockchain systems and networks. Several promising open issues are also summarized finally for future research directions. We wish this survey can serve as a useful guideline for researchers, engineers, and educators about the cutting-edge development of blockchains in the perspectives of theories, modelings, and tools.


id: cord-218639-ewkche9r
title: Multiscale statistical physics of the Human-SARS-CoV-2 interactome
author: Ghavasieh, Arsham
date: 2020-08-21
txt: ./txt/cord-218639-ewkche9r.txt
cache: ./cache/cord-218639-ewkche9r.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.09649v1.pdf
doi: nan
summary:
  Protein-protein interaction (PPI) networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID--19 and providing ground for drug repurposing strategies. Similarly, they have been used for characterizing the interactions between viral and human proteins in case of SARS-CoV-2 [13] [14] [15] , providing insights into the structure and function of the virus 16 and identifying drug repurposing strategies 17, 18 . Instead, we model the propagation of perturbations from viral nodes through the whole system, using bio-chemical and regulatory dynamics, to obtain the spreading patterns and compare the average impact of viruses on human proteins. Our results shed light on the unexplored aspects of SARS-CoV-2, from the perspective of statistical physics of complex networks, and the presented framework opens the doors for further theoretical developments aiming to characterize structure and dynamics of virus-host interactions, as well as grounds for further experimental investigation and potentially novel clinical treatments.

abstract:
  Protein-protein interaction (PPI) networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID--19 and providing ground for drug repurposing strategies. However, our knowledge of (dis)similarities between this one and other viral agents is still very limited. Here we compare the novel coronavirus PPI network against 45 known viruses, from the perspective of statistical physics. Our results show that classic analysis such as percolation is not sensitive to the distinguishing features of viruses, whereas the analysis of biochemical spreading patterns allows us to meaningfully categorize the viruses and quantitatively compare their impact on human proteins. Remarkably, when Gibbsian-like density matrices are used to represent each system's state, the corresponding macroscopic statistical properties measured by the spectral entropy reveals the existence of clusters of viruses at multiple scales. Overall, our results indicate that SARS-CoV-2 exhibits similarities to viruses like SARS-CoV and Influenza A at small scales, while at larger scales it exhibits more similarities to viruses such as HIV1 and HTLV1.


id: cord-220116-6i7kg4mj
title: Social distancing and epidemic resurgence in agent-based Susceptible-Infectious-Recovered models
author: Mukhamadiarov, Ruslan I.
date: 2020-06-03
txt: ./txt/cord-220116-6i7kg4mj.txt
cache: ./cache/cord-220116-6i7kg4mj.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.02552v1.pdf
doi: nan
summary:
  To determine the robustness of our results and compare the influence of different contact characteristics, we ran our stochastic model on four distinct spatially structured architectures, namely i) regular two-dimensional square lattices, wherein individuals move slowly and with limited range, i.e., spread diffusively; ii) two-dimensional small-world networks that in addition incorporate substantial long-distance interactions and contaminations; and finally on iii) random as well as iv) scale-free social contact networks. For both the two-dimensional regular lattice and small-world structure, a similar sudden drop in the total number of infected individuals ( Figure 6B ) requires a considerably longer mitigation duration: In these dynamical networks, the repopulation of nodes with infective individuals facilitates disease spreading, thereby diminishing control efficacy. In this study, we implemented social distancing control measures for simple stochastic SIR epidemic models on regular square lattices with diffusive spreading, two-dimensional Newman-Watts small-world networks that include highly infective long-distance connections, and static contact networks, either with random connectivity or scale-free topology.

abstract:
  Once an epidemic outbreak has been effectively contained through non-pharmaceutical interventions, a safe protocol is required for the subsequent release of social distancing restrictions to prevent a disastrous resurgence of the infection. We report individual-based numerical simulations of stochastic susceptible-infectious-recovered model variants on four distinct spatially organized lattice and network architectures wherein contact and mobility constraints are implemented. We robustly find that the intensity and spatial spread of the epidemic recurrence wave can be limited to a manageable extent provided release of these restrictions is delayed sufficiently (for a duration of at least thrice the time until the peak of the unmitigated outbreak) and long-distance connections are maintained on a low level (limited to less than five percent of the overall connectivity).


id: cord-225177-f7i0sbwt
title: Characterizing information leaders in Twitter during COVID-19 crisis
author: Pastor-Escuredo, David
date: 2020-05-14
txt: ./txt/cord-225177-f7i0sbwt.txt
cache: ./cache/cord-225177-f7i0sbwt.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2005.07266v1.pdf
doi: nan
summary:
  Infodemics are frequent specially in social networks that are distributed systems of information generation and spreading. However, in social media, besides content, people''s individual behavior and network properties, dynamics and topology are other relevant factors that determine the spread of information through the network [21] [22] [23] . Centrality metrics are used to identify relevant nodes that are further characterized in terms of users'' parameters managed by Twitter [25] [26] [27] [28] [29] . The current flow betweenness shows an unconnected graph which is very interesting as decentralized nodes play a key role in transporting information through the network (see Fig. 6 ). The current flow closeness shows also an unconnected graph which means that the social network is rather homogeneously distributed overall with parallel communities of information that do not necessarily interact with each other (see Fig. 7 ).

abstract:
  Information is key during a crisis such as the current COVID-19 pandemic as it greatly shapes people opinion, behaviour and even their psychological state. It has been acknowledged from the Secretary-General of the United Nations that the infodemic of misinformation is an important secondary crisis produced by the pandemic. Infodemics can amplify the real negative consequences of the pandemic in different dimensions: social, economic and even sanitary. For instance, infodemics can lead to hatred between population groups that fragment the society influencing its response or result in negative habits that help the pandemic propagate. On the contrary, reliable and trustful information along with messages of hope and solidarity can be used to control the pandemic, build safety nets and help promote resilience and antifragility. We propose a framework to characterize leaders in Twitter based on the analysis of the social graph derived from the activity in this social network. Centrality metrics are used to identify relevant nodes that are further characterized in terms of users parameters managed by Twitter. We then assess the resulting topology of clusters of leaders. Although this tool may be used for surveillance of individuals, we propose it as the basis for a constructive application to empower users with a positive influence in the collective behaviour of the network and the propagation of information.


id: cord-230294-bjy2ixcj
title: #lockdown: network-enhanced emotional profiling at the times of COVID-19
author: Stella, Massimo
date: 2020-05-09
url: https://arxiv.org/pdf/2005.04404v1.pdf
doi: nan
abstract:
  The COVID-19 pandemic forced countries all over the world to take unprecedented measures like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap through social media by introducing MERCURIAL (Multi-layer Co-occurrence Networks for Emotional Profiling), a framework which exploits linguistic networks of words and hashtags to reconstruct social discourse describing real-world events. We use MERCURIAL to analyse 101,767 tweets from Italy, the first country to react to the COVID-19 threat with a nationwide lockdown. The data were collected between 11th and 17th March, immediately after the announcement of the Italian lockdown and the WHO declaring COVID-19 a pandemic. Our analysis provides unique insights into the psychological burden of this crisis, focussing on: (i) the Italian official campaign for self-quarantine (#iorestoacasa}), (ii) national lockdown (#italylockdown), and (iii) social denounce (#sciacalli). Our exploration unveils evidence for the emergence of complex emotional profiles, where anger and fear (towards political debates and socio-economic repercussions) coexisted with trust, solidarity, and hope (related to the institutions and local communities). We discuss our findings in relation to mental well-being issues and coping mechanisms, like instigation to violence, grieving, and solidarity. We argue that our framework represents an innovative thermometer of emotional status, a powerful tool for policy makers to quickly gauge feelings in massive audiences and devise appropriate responses based on cognitive data.


id: cord-234918-puunbcio
author: Shalu, Hrithwik
date: 2020-08-19
txt: ./txt/cord-234918-puunbcio.txt
cache: ./cache/cord-234918-puunbcio.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.08912v1.pdf
doi: nan
summary:
  The scarcity of training data and class imbalance issues were effectively tackled in our approach by the use of Data Augmentation Generative Adversarial Network (DAGAN) and model architecture based as a Convolutional Siamese Network with attention mechanism. In [9] the authors proposed a modified CNN based on class decomposition, termed as Decompose Transfer Compose model to improve the performance of pre-trained models on the detection of COVID-19 cases from chest x-ray images. In [34] the authors proposed a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset. Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset

title:
  A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays

abstract:
  This paper introduces a paradigm of smartphone application based disease diagnostics that may completely revolutionise the way healthcare services are being provided. Although primarily aimed to assist the problems in rendering the healthcare services during the coronavirus pandemic, the model can also be extended to identify the exact disease that the patient is caught with from a broad spectrum of pulmonary diseases. The app inputs Chest X-Ray images captured from the mobile camera which is then relayed to the AI architecture in a cloud platform, and diagnoses the disease with state of the art accuracy. Doctors with a smartphone can leverage the application to save the considerable time that standard COVID-19 tests take for preliminary diagnosis. The scarcity of training data and class imbalance issues were effectively tackled in our approach by the use of Data Augmentation Generative Adversarial Network (DAGAN) and model architecture based as a Convolutional Siamese Network with attention mechanism. The backend model was tested for robustness us-ing publicly available datasets under two different classification scenarios(Binary/Multiclass) with minimal and noisy data. The model achieved pinnacle testing accuracy of 99.30% and 98.40% on the two respective scenarios, making it completely reliable for its users. On top of that a semi-live training scenario was introduced, which helps improve the app performance over time as data accumulates. Overall, the problems of generalisability of complex models and data inefficiency is tackled through the model architecture. The app based setting with semi live training helps in ease of access to reliable healthcare in the society, as well as help ineffective research of rare diseases in a minimal data setting.


id: cord-241057-cq20z1jt
title: Statistical Physics of Epidemic on Network Predictions for SARS-CoV-2 Parameters
author: Han, Jungmin
date: 2020-07-06
txt: ./txt/cord-241057-cq20z1jt.txt
cache: ./cache/cord-241057-cq20z1jt.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.03101v1.pdf
doi: nan
summary:
  We reformulated this problem as the statistical physics of independent location-specific ''balls'' attached to every model in a six-dimensional lattice of 56448 parametrized models by elastic springs, with model-specific ''spring constants'' determined by the stochasticity of network epidemic simulations for that model. The first problem that one must contend with is that even rough estimates of the high infection transmission rate and a death rate with strong age dependence imply that one must use large networks for simulations, on the order of 10 5 nodes, because one must avoid finite-size effects in order to accurately fit the early stochastic events. Finally, we simulated the effects of various partially effective social-distancing measures on random networks and parameter sets given by the posterior expectation values of our Bayes model comparison. We compared the posterior expectation for this parameter for a location with the actual population density in an attempt to predict the appropriate way to incorporate measurable population densities in epidemic on network models [37, 38] .

abstract:
  The SARS-CoV-2 pandemic has necessitated mitigation efforts around the world. We use only reported deaths in the two weeks after the first death to determine infection parameters, in order to make predictions of hidden variables such as the time dependence of the number of infections. Early deaths are sporadic and discrete so the use of network models of epidemic spread is imperative, with the network itself a crucial random variable. Location-specific population age distributions and population densities must be taken into account when attempting to fit these events with parametrized models. These characteristics render naive Bayesian model comparison impractical as the networks have to be large enough to avoid finite-size effects. We reformulated this problem as the statistical physics of independent location-specific `balls' attached to every model in a six-dimensional lattice of 56448 parametrized models by elastic springs, with model-specific `spring constants' determined by the stochasticity of network epidemic simulations for that model. The distribution of balls then determines all Bayes posterior expectations. Important characteristics of the contagion are determinable: the fraction of infected patients that die ($0.017pm 0.009$), the expected period an infected person is contagious ($22 pm 6$ days) and the expected time between the first infection and the first death ($25 pm 8$ days) in the US. The rate of exponential increase in the number of infected individuals is $0.18pm 0.03$ per day, corresponding to 65 million infected individuals in one hundred days from a single initial infection, which fell to 166000 with even imperfect social distancing effectuated two weeks after the first recorded death. The fraction of compliant socially-distancing individuals matters less than their fraction of social contact reduction for altering the cumulative number of infections.


id: cord-253711-a0prku2k
author: Mao, Liang
date: 2011-11-26
url: https://www.sciencedirect.com/science/article/pii/S0277953611006551
doi: 10.1016/j.socscimed.2011.10.012
title:
  Coupling infectious diseases, human preventive behavior, and networks – A conceptual framework for epidemic modeling

abstract:
  Human-disease interactions involve the transmission of infectious diseases among individuals and the practice of preventive behavior by individuals. Both infectious diseases and preventive behavior diffuse simultaneously through human networks and interact with one another, but few existing models have coupled them together. This article proposes a conceptual framework to fill this knowledge gap and illustrates the model establishment. The conceptual model consists of two networks and two diffusion processes. The two networks include: an infection network that transmits diseases and a communication network that channels inter-personal influence regarding preventive behavior. Both networks are composed of same individuals but different types of interactions. This article further introduces modeling approaches to formulize such a framework, including the individual-based modeling approach, network theory, disease transmission models and behavioral models. An illustrative model was implemented to simulate a coupled-diffusion process during an influenza epidemic. The simulation outcomes suggest that the transmission probability of a disease and the structure of infection network have profound effects on the dynamics of coupled-diffusion. The results imply that current models may underestimate disease transmissibility parameters, because human preventive behavior has not been considered. This issue calls for a new interdisciplinary study that incorporates theories from epidemiology, social science, behavioral science, and health psychology.


id: cord-256707-kllv27bl
title: Evolution of Chinese airport network
author: Zhang, Jun
date: 2010-09-15
txt: ./txt/cord-256707-kllv27bl.txt
cache: ./cache/cord-256707-kllv27bl.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0378437110004723
doi: 10.1016/j.physa.2010.05.042
summary:
  It is found that although the topology of CAN has remained steady during the past few years, there are many dynamic switchings inside the network, which have changed the relative importance of airports and airlines. As the aviation industry is an important indicator of economic growth, it is necessary and very meaningful to investigate the evolution of the airport network. He also found the network structure is dynamic, with changes in the importance of airports and airlines, and the traffic on BAN has doubled during a period in which the topology of BAN has shrunk [44] . Inspired by their interesting work, we investigate the evolution of Chinese Airport Network (CAN) from the year 1950 to 2008 (1991-2008 for detailed traffic information and 2002-2009 for detailed topology information). In summary, we investigate the evolution of Chinese airport network (CAN), including the topology, the traffic and the interplay between them.

abstract:
  With the rapid development of the economy and the accelerated globalization process, the aviation industry plays a more and more critical role in today’s world, in both developed and developing countries. As the infrastructure of aviation industry, the airport network is one of the most important indicators of economic growth. In this paper, we investigate the evolution of the Chinese airport network (CAN) via complex network theory. It is found that although the topology of CAN has remained steady during the past few years, there are many dynamic switchings inside the network, which have changed the relative importance of airports and airlines. Moreover, we investigate the evolution of traffic flow (passengers and cargoes) on CAN. It is found that the traffic continues to grow in an exponential form and has evident seasonal fluctuations. We also found that cargo traffic and passenger traffic are positively related but the correlations are quite different for different kinds of cities.


id: cord-256713-tlluxd11
title: Is Network Clustering Detectable in Transmission Trees?
author: Welch, David
date: 2011-06-03
txt: ./txt/cord-256713-tlluxd11.txt
cache: ./cache/cord-256713-tlluxd11.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/21731813/
doi: 10.3390/v3060659
summary:
  [15] show that for a class of networks known as random intersection graphs in which individuals belong to one or more overlapping groups and groups form fully connected cliques, an increase in clustering reduces the epidemic threshold, that is, major outbreaks may occur at lower levels of transmissibility in highly clustered networks. They demonstrate that a rewiring of random intersection graphs that preserves the degree sequence but decreases clustering produces networks with similarly lowered epidemic thresholds and even smaller mean outbreak sizes. From a statistical point of view, these results indicate that even with full data from a particular epidemic outbreak, such as complete knowledge of the transmission tree, it is unlikely that the level of clustering in the underlying contact network could be accurately inferred independently of the degree distribution.

abstract:
  Networks are often used to model the contact processes that allow pathogens to spread between hosts but it remains unclear which models best describe these networks. One question is whether clustering in networks, roughly defined as the propensity for triangles to form, affects the dynamics of disease spread. We perform a simulation study to see if there is a signal in epidemic transmission trees of clustering. We simulate susceptible-exposed-infectious-removed (SEIR) epidemics (with no re-infection) over networks with fixed degree sequences but different levels of clustering and compare trees from networks with the same degree sequence and different clustering levels. We find that the variation of such trees simulated on networks with different levels of clustering is barely greater than those simulated on networks with the same level of clustering, suggesting that clustering can not be detected in transmission data when re-infection does not occur.


id: cord-259634-ays40jlz
author: Marcelino, Jose
date: 2012-05-15
txt: ./txt/cord-259634-ays40jlz.txt
cache: ./cache/cord-259634-ays40jlz.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/1205.3245v1.pdf
doi: 10.1371/4f8c9a2e1fca8
summary:
  Here we expand on this finding further by considering a range of centrality measures for individual connections between cities, show that their targeted removal can improve on existing control strategies [5] for controlling influenza spreading and finally discuss the effect of the community structure on this control. To demonstrate the impact on influenza spreading caused by topological changes to the airline network, we run simulations using a stochastic metapopulation model of influenza [22] [23] where the worldwide network of commercial flights is used as the path for infected individuals traveling between cities (see Fig. 1A with Mexico City as starting node of an outbreak). Applying the same spreading simulations on these rewired versions of the network showed that only on networks that preserved the original''s community structure did we observe a significant reduction in infections when removing edges (see Fig. 3 ) connecting nodes ranked by Jaccard coefficient.

title:
  Critical paths in a metapopulation model of H1N1: Efficiently delaying influenza spreading through flight cancellation

abstract:
  Disease spreading through human travel networks has been a topic of great interest in recent years, as witnessed during outbreaks of influenza A (H1N1) or SARS pandemics. One way to stop spreading over the airline network are travel restrictions for major airports or network hubs based on the total number of passengers of an airport. Here, we test alternative strategies using edge removal, cancelling targeted flight connections rather than restricting traffic for network hubs, for controlling spreading over the airline network. We employ a SEIR metapopulation model that takes into account the population of cities, simulates infection within cities and across the network of the top 500 airports, and tests different flight cancellation methods for limiting the course of infection. The time required to spread an infection globally, as simulated by a stochastic global spreading model was used to rank the candidate control strategies. The model includes both local spreading dynamics at the level of populations and long-range connectivity obtained from real global airline travel data. Simulated spreading in this network showed that spreading infected 37% less individuals after cancelling a quarter of flight connections between cities, as selected by betweenness centrality. The alternative strategy of closing down whole airports causing the same number of cancelled connections only reduced infections by 18%. In conclusion, selecting highly ranked single connections between cities for cancellation was more effective, resulting in fewer individuals infected with influenza, compared to shutting down whole airports. It is also a more efficient strategy, affecting fewer passengers while producing the same reduction in infections. The network of connections between the top 500 airports is available under the resources link on our website http://www.biological-networks.org.


id: cord-262100-z6uv32a0
author: Wang, Yuanyuan
date: 2020-09-14
txt: ./txt/cord-262100-z6uv32a0.txt
cache: ./cache/cord-262100-z6uv32a0.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32929212/
doi: 10.1038/s41380-020-00881-6
summary:
  Noticeably, psychomotor symptoms such as impaired motor skills, restlessness, and inability to relax exhibited high centrality during the outbreak, which still relatively high but showed substantial remission during after peak stage (in terms of strength, betweenness, or bridge centrality). This study provides novel insights into the changes in central features during the different COVID-19 stages and highlights motor-related symptoms as bridge symptoms, which could activate the connection between anxiety and depression. In a recent longitudinal study on mental health during COVID-19, no significant changes in anxiety and depression were found in the general Chinese population between the initial outbreak and the after peak period [6] . However, the existing studies did not investigate the mechanism and changes in anxiety and depressive symptoms throughout the COVID-19 outbreak and the after peak using network analysis. During the outbreak and after peak, the occurrence of either impaired motor skills with depression symptoms or restlessness with anxiety symptoms could increase the risk of activation for other mental disorders.

title:
  Changes in network centrality of psychopathology symptoms between the COVID-19 outbreak and after peak

abstract:
  The current study investigated the mechanism and changes in psychopathology symptoms throughout the COVID-19 outbreak and after peak. Two studies were conducted separately in China during outbreak and the after peak stages, with 2540 participants were recruited from February 6 to 16, 2020, and 2543 participants were recruited from April 25 to May 5, 2020. The network models were created to explore the relationship between psychopathology symptoms both within and across anxiety and depression, with anxiety measured by the Generalized Anxiety Disorder-7 and depression measured by the Patient Health Questionnaire-9. Symptom network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between the outbreak and after peak. Noticeably, psychomotor symptoms such as impaired motor skills, restlessness, and inability to relax exhibited high centrality during the outbreak, which still relatively high but showed substantial remission during after peak stage (in terms of strength, betweenness, or bridge centrality). Meanwhile, symptoms of irritability (strength, betweenness, or bridge centrality) and loss of energy (bridge centrality) played an important role in the network after the peak of the pandemic. This study provides novel insights into the changes in central features during the different COVID-19 stages and highlights motor-related symptoms as bridge symptoms, which could activate the connection between anxiety and depression. The results revealed that restrictions on movement were associated with worsen in psychomotor symptoms, indicating that future psychological interventions should target motor-related symptoms as priority.


id: cord-266771-zesp6q0w
title: Complex networks to understand the past: the case of roads in Bourbon Spain
author: Pablo-Martí, Federico
date: 2020-10-06
url: https://www.ncbi.nlm.nih.gov/pubmed/33042288/
doi: 10.1007/s11698-020-00218-x
abstract:
  The work aims to study, using GIS techniques and network analysis, the development of the road network in Spain during the period between the War of Succession and the introduction of the railway (1700–1850). Our research is based on a detailed cartographic review of maps made during the War of Succession, largely improving preexisting studies based on books of itineraries from the sixteenth century onwards. We build a new, complete map of the main roads at the beginning of the eighteenth century along with the matrix of transport costs for all the important towns describing the communications network. Our study of this complex network, supplemented by a counterfactual analysis carried out using a simulation model based on agents using different centralized decision-making processes, allows us to establish three main results. First, existing trade flows at the beginning of the eighteenth century had a radial structure, so the Bourbon infrastructure plan only consolidated a preexisting situation. Second, the development of the network did not suppose important alterations in the comparative centrality of the regions. Finally, the design of the paved road network was adequate for the economic needs of the country. These findings are in stark contrast with claims that the radial structure of the Bourbon roads was designed ex-novo with political or ideological objectives rather than economic ones. Our methodology paves the way to further studies of path-dependent, long-term processes of network design as the key to understanding the true origin of many currently existing situations.


id: cord-269711-tw5armh8
title: The importance of contact network topology for the success of vaccination strategies
author: Ma, Junling
date: 2013-05-21
txt: ./txt/cord-269711-tw5armh8.txt
cache: ./cache/cord-269711-tw5armh8.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/23376579/
doi: 10.1016/j.jtbi.2013.01.006
summary:
  Abstract The effects of a number of vaccination strategies on the spread of an SIR type disease are numerically investigated for several common network topologies including random, scale-free, small world, and meta-random networks. These strategies, namely, prioritized, random, follow links and contact tracing, are compared across networks using extensive simulations with disease parameters relevant for viruses such as pandemic influenza H1N1/09. (2006) compared the efficacy of contact tracing on random and scale-free networks and found that for transmission rates greater than a certain threshold, the final epidemic size is smaller on a scale-free network than on a corresponding random network, while they considered the effects of degree correlations in Kiss et al. We investigate numerically whether network topologies affect the effectiveness of vaccination strategies started with a delay after the disease is widespread; for example, a 40 day delay as in the second wave of the 2009 influenza pandemic in British Columbia, Canada (Office of the Provincial Health Officer, 2010).

abstract:
  Abstract The effects of a number of vaccination strategies on the spread of an SIR type disease are numerically investigated for several common network topologies including random, scale-free, small world, and meta-random networks. These strategies, namely, prioritized, random, follow links and contact tracing, are compared across networks using extensive simulations with disease parameters relevant for viruses such as pandemic influenza H1N1/09. Two scenarios for a network SIR model are considered. First, a model with a given transmission rate is studied. Second, a model with a given initial growth rate is considered, because the initial growth rate is commonly used to impute the transmission rate from incidence curves and to predict the course of an epidemic. Since a vaccine may not be readily available for a new virus, the case of a delay in the start of vaccination is also considered in addition to the case of no delay. It is found that network topology can have a larger impact on the spread of the disease than the choice of vaccination strategy. Simulations also show that the network structure has a large effect on both the course of an epidemic and the determination of the transmission rate from the initial growth rate. The effect of delay in the vaccination start time varies tremendously with network topology. Results show that, without the knowledge of network topology, predictions on the peak and the final size of an epidemic cannot be made solely based on the initial exponential growth rate or transmission rate. This demonstrates the importance of understanding the topology of realistic contact networks when evaluating vaccination strategies.


id: cord-273941-gu6nnv9d
title: Chapter 5 Network Pharmacology
author: Chandran, Uma
date: 2017-12-31
url: https://api.elsevier.com/content/article/pii/B9780128018149000052
doi: 10.1016/b978-0-12-801814-9.00005-2
abstract:
  Abstract The one-drug/one-target/one-disease approach to drug discovery is presently facing many challenges of safety, efficacy, and sustainability. Network biology and polypharmacology approaches gained appreciation recently as methods for omics data integration and multitarget drug development, respectively. The combination of these two approaches created a novel paradigm called network pharmacology (NP) that looks at the effect of drugs on both the interactome and the diseasome level. Ayurveda, the traditional system of Indian medicine, uses intelligent formulations containing multiple ingredients and multiple bioactive compounds; however, the scientific rationale and mechanisms remain largely unexplored. NP approaches can serve as a valuable tool for evidence-based Ayurveda to understand the medicines’ putative actions, indications, and mechanisms. This chapter discusses NP and its potential to explore traditional medicine systems to overcome the drug discovery impasse.


id: cord-276178-0hrs1w7r
author: Bangotra, Deep Kumar
date: 2020-07-13
url: https://doi.org/10.3390/s20143887
doi: 10.3390/s20143887
title:
  An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare

abstract:
  The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.


id: cord-280648-1dpsggwx
title: Regulation, competition and network evolution in aviation
author: Gillen, David
date: 2005-05-31
txt: ./txt/cord-280648-1dpsggwx.txt
cache: ./cache/cord-280648-1dpsggwx.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S096969970500030X
doi: 10.1016/j.jairtraman.2005.03.002
summary:
  The organization of production spatially in air transportation networks confers both demand and supply side network economies and the choice of network structure by a carrier necessarily reflects aspects of its business model and will exhibit different revenue and cost drivers. Like the FSA model, the VBA business plan creates a network structure that can promote connectivity but in contrast trades off lower levels of service, measured both in capacity and frequency, against lower fares. The entrenched FSA carriers'' focuses on developing hub and spoke networks while new entrants seem intent on creating low-cost, point-to-point structures. The resulting market structure of competition between FSAs was thus a cozy oligopoly in which airlines competed on prices for some economy fares, but practiced complex price discrimination that allowed high yields on business travel.

abstract:
  Abstract Our focus is the evolution of business strategies and network structure decisions in the commercial passenger aviation industry. The paper reviews the growth of hub-and-spoke networks as the dominant business model following deregulation in the latter part of the 20th century, followed by the emergence of value-based airlines as a global phenomenon at the end of the century. The paper highlights the link between airline business strategies and network structures, and examines the resulting competition between divergent network structure business models. In this context we discuss issues of market structure stability and the role played by competition policy.


id: cord-282035-jibmg4ch
author: Dunbar, R. I. M.
date: 2020-08-26
url: https://doi.org/10.1098/rspa.2020.0446
doi: 10.1098/rspa.2020.0446
title:
  Structure and function in human and primate social networks: implications for diffusion, network stability and health

abstract:
  The human social world is orders of magnitude smaller than our highly urbanized world might lead us to suppose. In addition, human social networks have a very distinct fractal structure similar to that observed in other primates. In part, this reflects a cognitive constraint, and in part a time constraint, on the capacity for interaction. Structured networks of this kind have a significant effect on the rates of transmission of both disease and information. Because the cognitive mechanism underpinning network structure is based on trust, internal and external threats that undermine trust or constrain interaction inevitably result in the fragmentation and restructuring of networks. In contexts where network sizes are smaller, this is likely to have significant impacts on psychological and physical health risks.


id: cord-283793-ab1msb2m
title: Modeling and analysis of epidemic spreading on community network with node's birth and death
author: Chanchan, Li
date: 2016-10-31
url: https://api.elsevier.com/content/article/pii/S1005888516600614
doi: 10.1016/s1005-8885(16)60061-4
abstract:
  Abstract In this paper, a modified susceptible infected susceptible (SIS) epidemic model is proposed on community structure networks considering birth and death of node. For the existence of node's death would change the topology of global network, the characteristic of network with death rate is discussed. Then we study the epidemiology behavior based on the mean-field theory and derive the relationships between epidemic threshold and other parameters, such as modularity coefficient, birth rate and death rates (caused by disease or other reasons). In addition, the stability of endemic equilibrium is analyzed. Theoretical analysis and simulations show that the epidemic threshold increases with the increase of two kinds of death rates, while it decreases with the increase of the modularity coefficient and network size.


id: cord-285522-3gv6469y
title: Social big data: Recent achievements and new challenges
author: Bello-Orgaz, Gema
date: 2015-08-28
url: https://doi.org/10.1016/j.inffus.2015.08.005
doi: 10.1016/j.inffus.2015.08.005
abstract:
  Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.


id: cord-285647-9tegcrc3
author: Estrada, Ernesto
date: 2020-08-17
txt: ./txt/cord-285647-9tegcrc3.txt
cache: ./cache/cord-285647-9tegcrc3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32872802/
doi: 10.1063/5.0015626
summary:
  By following the main subdiffusive routes across the PPI network, we identify proteins mainly expressed in the heart, cerebral cortex, thymus, testis, lymph node, kidney, among others of the organs reported to be affected by COVID-19. 25, 26 Therefore, we assume here that perturbations produced by SARS-CoV-2 proteins on the human PPI network are propagated by means of diffusive processes. Here, we propose the use of a time-fractional diffusion model on the PPI network of proteins targeted by SARS-CoV-2. We now consider how a perturbation produced by SARS-CoV-2 on a protein mainly expressed in the lungs can be propagated to proteins mainly located in other tissues (see Table S4 in the supplementary material) by a subdiffusive process. Here, we have studied the particular case in which the time-fractional diffusion equation produces a subdiffusive regime, with the use of α = 3/4 in the network of human proteins targeted by SARS-CoV-2.

title:
  Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS-CoV-2

abstract:
  The coronavirus 2019 (COVID-19) respiratory disease is caused by the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which uses the enzyme ACE2 to enter human cells. This disease is characterized by important damage at a multi-organ level, partially due to the abundant expression of ACE2 in practically all human tissues. However, not every organ in which ACE2 is abundant is affected by SARS-CoV-2, which suggests the existence of other multi-organ routes for transmitting the perturbations produced by the virus. We consider here diffusive processes through the protein–protein interaction (PPI) network of proteins targeted by SARS-CoV-2 as an alternative route. We found a subdiffusive regime that allows the propagation of virus perturbations through the PPI network at a significant rate. By following the main subdiffusive routes across the PPI network, we identify proteins mainly expressed in the heart, cerebral cortex, thymus, testis, lymph node, kidney, among others of the organs reported to be affected by COVID-19.


id: cord-285872-rnayrws3
author: Elgendi, Mohamed
date: 2020-08-18
txt: ./txt/cord-285872-rnayrws3.txt
cache: ./cache/cord-285872-rnayrws3.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.3389/fmed.2020.00550
doi: 10.3389/fmed.2020.00550
summary:
  Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. Sethy and Behera (8) explored 10 different pre-trained neural networks, reporting an accuracy of 93% on a balanced dataset, for detecting COVID-19 on X-ray images. Our study aims to determine the optimal learning method, by investigating different types of pre-trained networks on a balanced dataset, for COVID-19 testing. To determine the optimal existing pre-trained neural network for the detection of COVID-19, we used the CoronaHack-Chest X-Ray-Dataset. Inception-v3 and ShuffleNet achieved an overall validation accuracy below 90% suggesting that these neural networks are not robust enough for detecting COVID-19 compared to, for example, ResNet-50 and DarkNet-19. After investigating 17 different pre-trained neural networks, our results showed that DarkNet-19 is the optimal pre-trained deep learning network for detection of imaging patterns of COVID-19 pneumonia on chest radiographs.

title:
  The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias

abstract:
  Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.


id: cord-288024-1mw0k5yu
title: Entrepreneurial entry: The role of social media
author: Wang, Wei
date: 2020-09-29
txt: ./txt/cord-288024-1mw0k5yu.txt
cache: ./cache/cord-288024-1mw0k5yu.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S004016252031163X
doi: 10.1016/j.techfore.2020.120337
summary:
  Thus, we propose that trust propensity, an individual''s tendency to believe in others (Choi, 2019; Gefen et al., 2003) , moderates the relationship between social media use and entrepreneurial entry. Our findings reveal that social media use https://doi.org/10.1016/j.techfore.2020.120337 Received 8 August 2020; Accepted 21 September 2020 has a positive impact on entrepreneurial entry with individuals'' offline network serving as a partial mediator. Second, our study specified a mechanism for the impact of individuals'' social media use on entrepreneurial entry via their offline network and used instrumental variables to help infer the causality. Thus, with higher social media use, individuals will have an expanded offline social network, which provides them the resources needed for successful entrepreneurial entry. We believe trust propensity in social media moderates the impact of individuals'' social media use on entrepreneurial entry by influencing their ability to network with strangers and known associates.

abstract:
  Despite the exponential growth of social media use, whether and how social media use may affect entrepreneurial entry remains a key research gap. In this study we examine whether individuals’ social media use influences their entrepreneurial entry. Drawing on social network theory, we argue that social media use allows individuals to obtain valuable social capital, as indicated by their offline social network, which increases their entrepreneurial entry. We further posit the relationship between social media use and entrepreneurial entry depends on individuals’ trust propensity based on the nature of social media as weak ties. Our model was supported by a nationally representative survey of 18,873 adults in China over two years. As the first paper on the role of social media on entrepreneurial entry, we hope our research highlights and puts forward research intersecting social media and entrepreneurship.


id: cord-288342-i37v602u
title: Coupled disease–behavior dynamics on complex networks: A review
author: Wang, Zhen
date: 2015-07-08
url: https://www.sciencedirect.com/science/article/pii/S1571064515001372
doi: 10.1016/j.plrev.2015.07.006
abstract:
  It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.


id: cord-290033-oaqqh21e
title: A disconnected policy network: The UK''s response to the Sierra Leone Ebola epidemic
author: Georgalakis, James
date: 2020-02-13
txt: ./txt/cord-290033-oaqqh21e.txt
cache: ./cache/cord-290033-oaqqh21e.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0277953620300708
doi: 10.1016/j.socscimed.2020.112851
summary:
  This paper investigates whether the inclusion of social scientists in the UK policy network that responded to the Ebola crisis in Sierra Leone (2013–16) was a transformational moment in the use of interdisciplinary research. There are two questions I hope to address through a critical commentary on the events that unfolded and with social network analysis of the UK based research and policy network that emerged: i) How transformational was the UK policy response to Ebola in relation to changes in evidence use patterns and behaviours? It utilises interactive theories of evidence use, the study of whole networks and the analysis of the connections between individuals in policy and research communities (Nightingale and Cromby, 2002; Oliver and Faul, 2018) . This is worth considering when one observes how ERAP''s supply of research knowledge and the SAGE sub-committee for anthropologists only increased the homophily of the social science sub-community, leaving it weakly connected to the core policy network (Fig. 4.) .

abstract:
  This paper investigates whether the inclusion of social scientists in the UK policy network that responded to the Ebola crisis in Sierra Leone (2013–16) was a transformational moment in the use of interdisciplinary research. In contrast to the existing literature, that relies heavily on qualitative accounts of the epidemic and ethnography, this study tests the dynamics of the connections between critical actors with quantitative network analysis. This novel approach explores how individuals are embedded in social relationships and how this may affect the production and use of evidence. The meso-level analysis, conducted between March and June 2019, is based on the traces of individuals' engagement found in secondary sources. Source material includes policy and strategy documents, committee papers, meeting minutes and personal correspondence. Social network analysis software, UCINet, was used to analyse the data and Netdraw for the visualisation of the network. Far from being one cohesive community of experts and government officials, the network of 134 people was weakly held together by a handful of super-connectors. Social scientists’ poor connections to the government embedded biomedical community may explain why they were most successful when they framed their expertise in terms of widely accepted concepts. The whole network was geographically and racially almost entirely isolated from those affected by or directly responding to the crisis in West Africa. Nonetheless, the case was made for interdisciplinarity and the value of social science in emergency preparedness and response. The challenge now is moving from the rhetoric to action on complex infectious disease outbreaks in ways that value all perspectives equally.


id: cord-295307-zrtixzgu
author: Delgado-Chaves, Fernando M.
date: 2020-07-21
txt: ./txt/cord-295307-zrtixzgu.txt
cache: ./cache/cord-295307-zrtixzgu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32708319/
doi: 10.3390/genes11070831
summary:
  Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E [Formula: see text] compared to wild type animals. Among the different types of GNs, gene co-expression networks (GCNs) are widely used in the literature due to their computational simplicity and good performance in order to study biological processes or diseases [8] [9] [10] . In the present work mice samples were compared organ-wise depending on whether these corresponded to control, 3 d p.i. and 5 d p.i. The identification of DEG was performed using the Limma [63] R package, which provides non-parametric robust estimation of the gene expression variance. In this work four gene networks were reconstructed to model the genetic response MHV infection in two tissues, liver and spleen, and in two different genetic backgrounds, wild type and Ly6E ∆HSC .

title:
  Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

abstract:
  Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E [Formula: see text] compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E [Formula: see text] mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E [Formula: see text] mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.


id: cord-303197-hpbh4o77
title: The state of one health research across disciplines and sectors – a bibliometric analysis
author: Humboldt-Dachroeden, Sarah
date: 2020-06-06
txt: ./txt/cord-303197-hpbh4o77.txt
cache: ./cache/cord-303197-hpbh4o77.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S2352771420301087
doi: 10.1016/j.onehlt.2020.100146
summary:
  There is a growing interest in One Health, reflected by the rising number of publications relating to One Health literature, but also through zoonotic disease outbreaks becoming more frequent, such as Ebola, Zika virus and COVID-19. This paper uses bibliometric analysis to explore the state of One Health in academic literature, to visualise the characteristics and trends within the field through a network analysis of citation patterns and bibliographic links. This paper uses bibliometric analysis to explore the state of One Health in academic literature, to visualise between the disciplines of human medicine, veterinary medicine and environment still persist -even in the face of the One Health approach. Four clusters of authors emerged in the network (green: zoonoses and epidemiology; blue: biodiversity and ecohealth; purple: animal health, public health; red: policy-related disciplines).

abstract:
  There is a growing interest in One Health, reflected by the rising number of publications relating to One Health literature, but also through zoonotic disease outbreaks becoming more frequent, such as Ebola, Zika virus and COVID-19. This paper uses bibliometric analysis to explore the state of One Health in academic literature, to visualise the characteristics and trends within the field through a network analysis of citation patterns and bibliographic links. The analysis focuses on publication trends, co-citation network of scientific journals, co-citation network of authors, and co-occurrence of keywords. The bibliometric analysis showed an increasing interest for One Health in academic research. However, it revealed some thematic and disciplinary shortcomings, in particular with respect to the inclusion of environmental themes and social science insights pertaining to the implementation of One Health policies. The analysis indicated that there is a need for more applicable approaches to strengthen intersectoral collaboration and knowledge sharing. Silos between the disciplines of human medicine, veterinary medicine and environment still persist. Engaging researchers with different expertise and disciplinary backgrounds will facilitate a more comprehensive perspective where the human-animal-environment interface is not researched as separate entities but as a coherent whole. Further, journals dedicated to One Health or interdisciplinary research provide scholars the possibility to publish multifaceted research. These journals are uniquely positioned to bridge between fields, strengthen interdisciplinary research and create room for social science approaches alongside of medical and natural sciences.


id: cord-306654-kal6ylkd
author: Li, Yuhong
date: 2020-10-10
url: https://doi.org/10.1016/j.ejor.2020.09.053
doi: 10.1016/j.ejor.2020.09.053
title:
  Ripple Effect in the Supply Chain Network: Forward and Backward Disruption Propagation, Network Health and Firm Vulnerability

abstract:
  A local disruption can propagate to forward and downward through the material flow and eventually influence the entire supply chain network (SCN). This phenomenon of ripple effect, immensely existing in practice, has received great interest in recent years. Moreover, forward and backward disruption propagations became major stressors for SCNs during the COVID-19 pandemic triggered by simultaneous and sequential supply and demand disruptions. However, current literature has paid less attention to the different impacts of the directions of disruption propagation. This study examines the disruption propagation through simulating simple interaction rules of firms inside the SCN. Specifically, an agent-based computational model is developed to delineate the supply chain disruption propagation behavior. Then, we conduct multi-level quantitative analysis to explore the effects of forward and backward disruption propagation, moderated by network structure, network-level health and node-level vulnerability. Our results demonstrate that it is practically important to differentiate between forward and backward disruption propagation, as they are distinctive in the associated mitigation strategies and in the effects on network and individual firm performance. Forward disruption propagation generally can be mitigated by substitute and backup supply and has greater impact on firms serving the assembly role and on the supply/assembly networks, whereas backward disruption propagation is normally mitigated by flexible operation and distribution and has bigger impact on firms serving the distribution role and on distribution networks. We further analyze the investment strategies in a dual-focal supply network under disruption propagation. We provide propositions to facilitate decision-making and summarize important managerial implications.


id: cord-307735-6pf7fkvq
author: Walkey, Allan J.
date: 2020-04-29
url: https://www.ncbi.nlm.nih.gov/pubmed/32426754/
doi: 10.1097/cce.0000000000000113
title:
  The Viral Infection and Respiratory Illness Universal Study (VIRUS): An International Registry of Coronavirus 2019-Related Critical Illness

abstract:
  The coronavirus disease 2019 pandemic has disproportionally strained intensive care services worldwide. Large areas of uncertainly regarding epidemiology, physiology, practice patterns, and resource demands for patients with coronavirus disease 2019 require rapid collection and dissemination of data. We describe the conception and implementation of an intensive care database rapidly developed and designed to meet data analytic needs in response to the coronavirus disease 2019 pandemic—the multicenter, international Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study. DESIGN: Prospective cohort study and disease registry. SETTING: Multinational cohort of ICUs. PATIENTS: Critically ill patients with a diagnosis of coronavirus disease 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Within 2 weeks of conception of the Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study, study leadership was convened, registry case report forms were designed, electronic data entry set up, and more than 250 centers had submitted the protocol for institutional review board approval, with more than 100 cases entered. CONCLUSIONS: The Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study provides an example of a rapidly deployed, international, pandemic registry that seeks to provide near real-time analytics and information regarding intensive care treatments and outcomes for patients with coronavirus disease 2019.


id: cord-308249-es948mux
author: Dokuka, Sofia
date: 2020-07-27
url: https://doi.org/10.1371/journal.pone.0236737
doi: 10.1371/journal.pone.0236737
title:
  How academic achievement spreads: The role of distinct social networks in academic performance diffusion

abstract:
  Behavior diffusion through social networks is a key social process. It may be guided by various factors such as network topology, type of propagated behavior, and the strength of network connections. In this paper, we claim that the type of social interactions is also an important ingredient of behavioral diffusion. We examine the spread of academic achievements of first-year undergraduate students through friendship and study assistance networks, applying stochastic actor-oriented modeling. We show that informal social connections transmit performance while instrumental connections do not. The results highlight the importance of friendship in educational environments and contribute to debates on the behavior spread in social networks.


id: cord-312817-gskbu0oh
title: Spatiotemporal network structure among “friends of friends” reveals contagious disease process
author: Witte, Carmel
date: 2020-08-06
txt: ./txt/cord-312817-gskbu0oh.txt
cache: ./cache/cord-312817-gskbu0oh.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1371/journal.pone.0237168
doi: 10.1371/journal.pone.0237168
summary:
  These results provide empirical evidence that at least some avian mycobacteriosis infections are transmitted between birds, and provide new methods for detecting contagious processes in large-scale global network structures with indirect contacts, even when transmission pathways, timing of cases, or etiologic agents are unknown. Thus, the population represents a group of birds for which 1) a near-complete social network could be assembled from housing records that tracked dynamic movement over time, and 2) avian mycobacteriosis disease status could be determined for any bird that died. Although disease clustering among friends of friends could represent a contagious process, there is a possibility that some of the association could be explained by homophily, i.e., that connected birds could be more alike than the general bird population in terms of species, behavior, susceptibility, enclosure characteristics, etc. For this test, we evaluated disease clustering between a subject and its friends of friends from different enclosures that could not have transmitted infection based on the timing of the contact.

abstract:
  Disease transmission can be identified in a social network from the structural patterns of contact. However, it is difficult to separate contagious processes from those driven by homophily, and multiple pathways of transmission or inexact information on the timing of infection can obscure the detection of true transmission events. Here, we analyze the dynamic social network of a large, and near-complete population of 16,430 zoo birds tracked daily over 22 years to test a novel “friends-of-friends” strategy for detecting contagion in a social network. The results show that cases of avian mycobacteriosis were significantly clustered among pairs of birds that had been in direct contact. However, since these clusters might result due to correlated traits or a shared environment, we also analyzed pairs of birds that had never been in direct contact but were indirectly connected in the network via other birds. The disease was also significantly clustered among these friends of friends and a reverse-time placebo test shows that homophily could not be causing the clustering. These results provide empirical evidence that at least some avian mycobacteriosis infections are transmitted between birds, and provide new methods for detecting contagious processes in large-scale global network structures with indirect contacts, even when transmission pathways, timing of cases, or etiologic agents are unknown.


id: cord-314498-zwq67aph
title: Smart business networks: Concepts and empirical evidence
author: van Heck, Eric
date: 2009-05-15
txt: ./txt/cord-314498-zwq67aph.txt
cache: ./cache/cord-314498-zwq67aph.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0167923609001274
doi: 10.1016/j.dss.2009.05.002
abstract: nan
summary:
  The key characteristics of a smart business network are that it has the ability to "rapidly pick, plug, and play" to configure rapidly to meet a specific objective, for example, to react to a customer order or an unexpected situation (for example dealing with emergencies) [4] . This combination of "pick, plug, play and disperse" means that the fundamental organizing capabilities for a smart business network are: (1) the ability for quick connect and disconnect with an actor; (2) the selection and execution of business processes across the network; and (3) establishing the decision rules and the embedded logic within the business network. The four papers put forward new insights about the concept of smart business networks and also provide empirical evidence about the functioning and outcome of these business networks and its potential impact on networked decision making and decision support systems.


id: cord-317435-4yuw7jo3
title: Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
author: Zhou, Yadi
date: 2020-03-16
url: https://www.ncbi.nlm.nih.gov/pubmed/32194980/
doi: 10.1038/s41421-020-0153-3
abstract:
  Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “Complementary Exposure” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.


id: cord-318716-a525bu7w
author: van den Oord, Steven
date: 2020-06-02
url: https://doi.org/10.1111/puar.13256
doi: 10.1111/puar.13256
title:
  Network of networks: preliminary lessons from the Antwerp Port Authority on crisis management and network governance to deal with the COVID‐19 pandemic

abstract:
  In this article we describe and illustrate what we call a network of networks perspective and map the development of a Lead network of the Antwerp Port Authority that governs various organizations and networks in the port community before and during the COVID‐19 pandemic. We find that setting a collective focus and selective integration to be crucial in the creation and reproduction of an effective system to adequately deal with a wicked problem like the COVID‐19 pandemic. We use the findings on crisis management and network governance to engage practitioners and public policy planners to revisit current design and governance of organizational networks within organizational fields that have been hit by the COVID‐19 pandemic.


id: cord-319055-r16dd0vj
title: Development of an Acoustic System for UAV Detection †
author: Dumitrescu, Cătălin
date: 2020-08-28
url: https://doi.org/10.3390/s20174870
doi: 10.3390/s20174870
abstract:
  The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.


id: cord-319658-u0wjgw50
title: Structural host-microbiota interaction networks
author: Guven-Maiorov, Emine
date: 2017-10-12
url: https://doi.org/10.1371/journal.pcbi.1005579
doi: 10.1371/journal.pcbi.1005579
abstract:
  Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.


id: cord-322815-r82iphem
title: Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network
author: Zhang, Weiping
date: 2020-07-04
url: https://api.elsevier.com/content/article/pii/S1062940820301455
doi: 10.1016/j.najef.2020.101248
abstract:
  Abstract This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.


id: cord-324256-5tzup41p
title: Infectious diseases spreading on a metapopulation network coupled with its second-neighbor network
author: Feng, Shanshan
date: 2019-11-15
url: https://www.ncbi.nlm.nih.gov/pubmed/32287503/
doi: 10.1016/j.amc.2019.05.005
abstract:
  Traditional infectious diseases models on metapopulation networks focus on direct transportations (e.g., direct flights), ignoring the effect of indirect transportations. Based on global aviation network, we turn the problem of indirect flights into a question of second neighbors, and propose a susceptible-infectious-susceptible model to study disease transmission on a connected metapopulation network coupled with its second-neighbor network (SNN). We calculate the basic reproduction number, which is independent of human mobility, and we prove the global stability of disease-free and endemic equilibria of the model. Furthermore, the study shows that the behavior that all travelers travel along the SNN may hinder the spread of disease if the SNN is not connected. However, the behavior that individuals travel along the metapopulation network coupled with its SNN contributes to the spread of disease. Thus for an emerging infectious disease, if the real network and its SNN keep the same connectivity, indirect transportations may be a potential threat and need to be controlled. Our work can be generalized to high-speed train and rail networks, which may further promote other research on metapopulation networks.


id: cord-327401-om4f42os
author: Bombelli, Alessandro
date: 2020-08-11
txt: ./txt/cord-327401-om4f42os.txt
cache: ./cache/cord-327401-om4f42os.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0966692320304737
doi: 10.1016/j.jtrangeo.2020.102815
summary:
  Given that the dataset we collected refers to a time-span that covers a pre-and a pandemic period, we analyzed how network characteristics and connectivity evolved with time for the three integrators and, to have a more thorough analysis, for three other airlines relevant from a cargo perspective. In (Malighetti et al., 2019a) and (Malighetti et al., 2019b) the authors focused, respectively, on the European and Asian network structure of FedEx, UPS, DHL, and TNT (the analysis covers a time-period prior to the FedEx acquisition), which are based on a limited temporal dataset of one week. For the three integrator, we focused on cargo capacities along major connections and generated time-series using the AFT associated to each observation. In this paper, we provided a thorough analysis of the network structure of integrators FedEx, UPS, and DHL, using historical data from public sources and estimated cargo weight capacity between airports to model each network.

title:
  Integrators'' global networks: A topology analysis with insights into the effect of the COVID-19 pandemic

abstract:
  In this paper we propose, to the best of our knowledge, the first analysis of the global networks of integrators FedEx, UPS, and DHL using network science. While noticing that all three networks rely on a “hub-and-spoke” structure, the network configuration of DHL leans towards a multi-“hub-and-spoke” structure that reflects the different business strategy of the integrator. We also analyzed the robustness of the networks, identified the most critical airports per integrator, and assessed that the network of DHL is the most robust according to our definition of robustness. Finally, given the unprecedented historical time that the airline industry is facing at the moment of writing, we provided some insights into how the COVID-19 pandemic affected the global capacity of integrators and other cargo airlines. Our results suggest that full-cargo airlines and, much more dramatically, combination airlines were impacted by the pandemic. On the other hand, apart from fluctuations in offered capacity due to travel bans that were quickly recovered thanks to the resilience of their networks, integrators seem to have escaped the early months of the pandemic unscathed.


id: cord-327651-yzwsqlb2
title: Network inference from multimodal data: A review of approaches from infectious disease transmission
author: Ray, Bisakha
date: 2016-09-06
txt: ./txt/cord-327651-yzwsqlb2.txt
cache: ./cache/cord-327651-yzwsqlb2.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.jbi.2016.09.004
doi: 10.1016/j.jbi.2016.09.004
summary:
  In infectious disease transmission network inference, Bayesian inference frameworks have been primarily used to integrate data such as dates of pathogen sample collection and symptom report date, pathogen genome sequences, and locations of patients [24] [25] [26] . Pathogen genomic data can capture within-host pathogen diversity (the product of effective population size in a generation and the average pathogen replication time [25, 26] ) and dynamics or provide information critical to understanding disease transmission such as evidence of new transmission pathways that cannot be inferred from epidemiological data alone [40, 41] . As molecular epidemiology and infectious disease transmission are areas in which network inference methods have been developed for bringing together multimodal data we use this review to investigate the foundational work in this specific field. In this section we briefly review multimodal integration methods for combining pathogen genomic data and epidemiological data in a single analysis, for inferring infection transmission trees and epidemic dynamic parameters.

abstract:
  Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.


id: cord-328858-6xqyllsl
author: Tajeddini, Kayhan
date: 2020-07-15
txt: ./txt/cord-328858-6xqyllsl.txt
cache: ./cache/cord-328858-6xqyllsl.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0278431920301572
doi: 10.1016/j.ijhm.2020.102605
summary:
  Utilizing a sample of 192 hospitality firms, this study investigates the moderating role of a dynamic environment, coupled with business and social networking ties and technology resources, on the relationship between entrepreneurial orientation and organizational performance in hospitality firms. Utilizing data gathered from 192 Japanese hospitality firms, this research offers and examines plausible assumptions concerning the interactive impacts of EO, dynamic environment and networking on service company growth and financial return. Utilizing the data gathered from Japanese hospitality firms, the findings clearly identified that in uncertain, dynamic environments, a higher level of risk and entrepreneurial orientation benefited business performance especially when coupled with strong business and social networks. This research is timely for the hospitality industry because it developed and tested an empirical model for explaining the relationship between dynamic environment, networking, technology resources, entrepreneurial orientation and organizational performance.

title:
  Enhancing hospitality business performance: The role of entrepreneurial orientation and networking ties in a dynamic environment

abstract:
  Utilizing a sample of 192 hospitality firms, this study investigates the moderating role of a dynamic environment, coupled with business and social networking ties and technology resources, on the relationship between entrepreneurial orientation and organizational performance in hospitality firms. This research is novel in that we adopt business network ties and social network ties as two moderating variables along with technology resources between entrepreneurial orientation and business performance, providing evidence on a topic which has received little attention to date. The results posit that in an uncertain, dynamic environment a higher level of risk and entrepreneurial orientation benefit business performance especially when coupled with strong business and social networks.


id: cord-332313-9m2iozj3
title: Structural efficiency to manipulate public research institution networks
author: Yang, Hyeonchae
date: 2016-01-13
url: https://doi.org/10.1016/j.techfore.2015.12.012
doi: 10.1016/j.techfore.2015.12.012
abstract:
  With the rising use of network analysis in the public sector, researchers have recently begun paying more attention to the management of entities from a network perspective. However, guiding elements in a network is difficult because of their complex and dynamic states. In a bid to address the issues involved in achieving network-wide outcomes, our work here sheds new light on quantifying structural efficiency to control inter-organizational networks maintained by public research institutions. In doing so, we draw attention to the set of subordinates suitable as change initiators to influence the entire research profiles of subordinates from three major public research institutions: the Government-funded Research Institutes (GRIs) in Korea, the Max-Planck-Gesellschaft (MPG) in Germany, and the National Laboratories (NLs) in the United States. Building networks on research similarities in portfolios, we investigate these networks with respect to their structural efficiency and topological properties. According to our estimation, only less than 30% of nodes are sufficient to initiate a cascade of changes throughout the network across institutions. The subunits that drive the network exhibit an inclination neither toward retaining a large number of connections nor toward having a long academic history. Our findings suggest that this structural efficiency indicator helps assess structural development or improvement plans for networks inside a multiunit public research institution.


id: cord-333088-ygdau2px
title: On representing network heterogeneities in the incidence rate of simple epidemic models
author: Roy, Manojit
date: 2006-03-31
txt: ./txt/cord-333088-ygdau2px.txt
cache: ./cache/cord-333088-ygdau2px.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S1476945X05000814
doi: 10.1016/j.ecocom.2005.09.001
summary:
  We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We also demonstrate the existence of a complex dynamical behavior in the stochastic system within the narrow small-world region, consisting of persistent cycles with enhanced amplitude and a well-defined period that are not predicted by the equivalent homogeneous mean-field model.

abstract:
  Abstract Mean-field ecological models ignore space and other forms of contact structure. At the opposite extreme, high-dimensional models that are both individual-based and stochastic incorporate the distributed nature of ecological interactions. In between, moment approximations have been proposed that represent the effect of correlations on the dynamics of mean quantities. As an alternative closer to the typical temporal models used in ecology, we present here results on “modified mean-field equations” for infectious disease dynamics, in which only mean quantities are followed and the effect of heterogeneous mixing is incorporated implicitly. We specifically investigate the previously proposed empirical parameterization of heterogeneous mixing in which the bilinear incidence rate SI is replaced by a nonlinear term kS p I q , for the case of stochastic SIRS dynamics on different contact networks, from a regular lattice to a random structure via small-world configurations. We show that, for two distinct dynamical cases involving a stable equilibrium and a noisy endemic steady state, the modified mean-field model approximates successfully the steady state dynamics as well as the respective short and long transients of decaying cycles. This result demonstrates that early on in the transients an approximate power-law relationship is established between global (mean) quantities and the covariance structure in the network. The approach fails in the more complex case of persistent cycles observed within the narrow range of small-world configurations.


id: cord-336747-8m7n5r85
title: Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study
author: Grossmann, G.
date: 2020-05-08
txt: ./txt/cord-336747-8m7n5r85.txt
cache: ./cache/cord-336747-8m7n5r85.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.05.05.20091736
doi: 10.1101/2020.05.05.20091736
summary:
  In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We calibrate both, ODE-models and stochastic models with interaction structure to the same basic reproduction number R 0 or to the same infection peak and compare the corresponding results. In the last decade, research focused largely on epidemic spreading, where interactions were constrained by contact networks, i.e. a graph representing the individuals (as nodes) and their connectivity (as edges). SIS-type models require knowledge of the spreading parameters (infection strength, recovery rate, etc.) and the contact network, which can partially be inferred from real-world observations. We are interested in the relationship between the contact network structure, R 0 , the height and time point of the infection-peak, and the number of individuals ultimately affected by the epidemic.

abstract:
  In the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time. In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic's dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODE-models. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number R0. We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.


id: cord-338127-et09wi82
author: Qin, Bosheng
date: 2020-09-14
url: https://www.ncbi.nlm.nih.gov/pubmed/32937867/
doi: 10.3390/s20185236
title:
  Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

abstract:
  The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.


id: cord-338588-rc1h4drd
title: Seven decades of chemotherapy clinical trials: a pan-cancer social network analysis
author: Li, Xuanyi
date: 2020-10-16
txt: ./txt/cord-338588-rc1h4drd.txt
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extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33067482/
doi: 10.1038/s41598-020-73466-6
summary:
  Seminal events (Fig. 1C) are likely a driver of preferential attachment 35 , and may The network is overwhelmingly dominated by men until 1980, when a trend towards increasing authorship by women begins to be seen; however, representation by women in first/last authorship remains low; gray shaded lines are 95% confidence intervals of the LOESS curves; (B) Men tend on average to have a longer productive period and to achieve a higher author impact score than women (P < 0.001 for both comparisons); (C) Men tend on average to be more central and have more collaborations outside of their subspecialty. While there is much to be applauded in the continued success of translating research findings into the clinic, we observed clear gender disparities within the cancer clinical trialist network: women have a statistically significantly lower final impact score, shorter productive period, less centrality, and less collaboration with those outside of their primary subspecialty.

abstract:
  Clinical trials establish the standard of cancer care, yet the evolution and characteristics of the social dynamics between the people conducting this work remain understudied. We performed a social network analysis of authors publishing chemotherapy-based prospective trials from 1946 to 2018 to understand how social influences, including the role of gender, have influenced the growth and development of this network, which has expanded exponentially from fewer than 50 authors in 1946 to 29,197 in 2018. While 99.4% of authors were directly or indirectly connected by 2018, our results indicate a tendency to predominantly connect with others in the same or similar fields, as well as an increasing disparity in author impact and number of connections. Scale-free effects were evident, with small numbers of individuals having disproportionate impact. Women were under-represented and likelier to have lower impact, shorter productive periods (P < 0.001 for both comparisons), less centrality, and a greater proportion of co-authors in their same subspecialty. The past 30 years were characterized by a trend towards increased authorship by women, with new author parity anticipated in 2032. The network of cancer clinical trialists is best characterized as strategic or mixed-motive, with cooperative and competitive elements influencing its appearance. Network effects such as low centrality, which may limit access to high-profile individuals, likely contribute to the observed disparities.


id: cord-340101-n9zqc1gm
title: The Neurobiology of Social Distance
author: Bzdok, Danilo
date: 2020-06-03
txt: ./txt/cord-340101-n9zqc1gm.txt
cache: ./cache/cord-340101-n9zqc1gm.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S1364661320301406
doi: 10.1016/j.tics.2020.05.016
summary:
  These authors conducted a follow-up analysis of 70 studies of longevity in older people, which followed ~3.5 million people over an average of ~7 years [16] : social isolation, living alone and feeling lonely increased the chances of dying by about 30%, even after accounting for age, sex and health status. There is now a wealth of evidence from long-term field studies of wild baboons that socially wellconnected females experience less harassment by other monkeys [7, 23] , have lower levels of cortisol stress hormones [25, 26] , faster wound healing [27] , produce more offspring and live longer [28] [29] [30] [31] . The perspective of brain network integration in loneliness was investigated in a seminal neuroimaging study of intrinsic functional connectivity in ~1,000 humans [124] . In humans, a longitudinal neuroimaging study indeed showed that social support from the mother promotes volume growth trajectories in the hippocampus, and predicts socioemotional development and emotion regulation in early adolescence [141] .

abstract:
  Abstract Never before have we experienced social isolation on such a massive scale as we have in response to COVID-19. Yet we know that the social environment has a dramatic impact on our sense of life satisfaction and well-being. In times of distress, crisis, or disaster, human resilience depends on the richness and strength of social connections, as well as active engagement in groups and communities. Over recent years, evidence emerging from various disciplines has made it abundantly clear: loneliness may be the most potent threat to survival and longevity. Here, we highlight the benefits of social bonds, choreographies of bond creation and maintenance, as well as the neurocognitive basis of social isolation and its deep consequences for mental and physical health.


id: cord-340827-vx37vlkf
title: Chapter 14 Diffusion, Strategic Interaction, and Social Structure
author: Jackson, Matthew O.
date: 2011-12-31
url: https://api.elsevier.com/content/article/pii/B9780444531872000140
doi: 10.1016/b978-0-444-53187-2.00014-0
abstract:
  Abstract We provide an overview and synthesis of the literature on how social networks influence behaviors, with a focus on diffusion. We discuss some highlights from the empirical literature on the impact of networks on behaviors and diffusion. We also discuss some of the more prominent models of network interactions, including recent advances regarding interdependent behaviors, modeled via games on networks. JEL Classification Codes: D85, C72, L14, Z13


id: cord-342579-kepbz245
title: Global networks and global change-induced tipping points
author: Galaz, Victor
date: 2014-05-01
url: https://doi.org/10.1007/s10784-014-9253-6
doi: 10.1007/s10784-014-9253-6
abstract:
  The existence of “tipping points” in human–environmental systems at multiple scales—such as abrupt negative changes in coral reef ecosystems, “runaway” climate change, and interacting nonlinear “planetary boundaries”—is often viewed as a substantial challenge for governance due to their inherent uncertainty, potential for rapid and large system change, and possible cascading effects on human well-being. Despite an increased scholarly and policy interest in the dynamics of these perceived “tipping points,” institutional and governance scholars have yet to make progress on how to analyze in which ways state and non-state actors attempt to anticipate, respond, and prevent the transgression of “tipping points” at large scales. In this article, we use three cases of global network responses to what we denote as global change-induced “tipping points”—ocean acidification, fisheries collapse, and infectious disease outbreaks. Based on the commonalities in several research streams, we develop four working propositions: information processing and early warning, multilevel and multinetwork responses, diversity in response capacity, and the balance between efficiency and legitimacy. We conclude by proposing a simple framework for the analysis of the interplay between perceived global change-induced “tipping points,” global networks, and international institutions.


id: cord-343419-vl6gkoin
title: Quantitative mapping of scientific research—The case of electrical conducting polymer nanocomposite
author: Lee, Pei-Chun
date: 2010-07-10
url: https://www.ncbi.nlm.nih.gov/pubmed/32287409/
doi: 10.1016/j.techfore.2010.06.002
abstract:
  This study aims to understand knowledge structure both quantitatively and visually by integrating keyword analysis and social network analysis of scientific papers. The methodology proposed in this study is capable of creating a three-dimensional “Research focus parallelship network” and a “Keyword Co-occurrence Network”, together with a two-dimensional knowledge map. The network and knowledge map can be depicted differently by choosing different information for the network actor, i.e. country, institute, paper and keyword, to reflect knowledge structures from macro, to meso, to micro-levels. A total of 223 highly cited papers published by 142 institutes and 26 countries are analyzed in this study. China and the US are the two countries located at the core of knowledge structure and China is ranked no. 1. This quantitative exploration provides a way to unveil important or emerging components in scientific development and also to visualize knowledge; thus an objective evaluation of scientific research is possible for quantitative technology management.


id: cord-346309-hveuq2x9
title: An Epidemiological Network Model for Disease Outbreak Detection
author: Reis, Ben Y
date: 2007-06-26
url: https://www.ncbi.nlm.nih.gov/pubmed/17593895/
doi: 10.1371/journal.pmed.0040210
abstract:
  BACKGROUND: Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most. METHODS AND FINDINGS: To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach. CONCLUSIONS: The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.


id: cord-346606-bsvlr3fk
title: The role of 5G for digital healthcare against COVID-19 pandemic: Opportunities and challenges
author: Siriwardhana, Yushan
date: 2020-11-04
txt: ./txt/cord-346606-bsvlr3fk.txt
cache: ./cache/cord-346606-bsvlr3fk.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S2405959520304744
doi: 10.1016/j.icte.2020.10.002
summary:
  The novel ICT technologies such as Internet of Things (IoT) [2] , Artificial Intelligence (AI) [3] , Big Data, 5G communications, cloud computing and blockchain [4] can play a vital role to facilitate the environment fostering protection and improvement of people and economies. These 5G technologies will enable ubiquitous digital health services combating COVID-19, described in the following section as 5G based healthcare use cases. Other applications would perform regular health monitoring of patients such as followup visits, provide instructions on medical services, and spread knowledge on present COVID-19 situation and upto date precautions. To address the issues in healthcare related supply chains, industries can adopt smart manufacturing techniques equipped with IoT sensor networks, automated production lines which dynamically adapt to the variations in demand, and sophisticated monitoring systems. Hence, solutions developed using 5G technologies serve various health related use cases such as telehealth, supply chain management, self-isolation and contact tracing, and rapid health services deployments.

abstract:
  COVID-19 pandemic caused a massive impact on healthcare, social life, and economies on a global scale. Apparently, technology has a vital role to enable ubiquitous and accessible digital health services in pandemic conditions as well as against “re-emergence” of COVID-19 disease in a post-pandemic era. Accordingly, 5G systems and 5G-enabled e-health solutions are paramount. This paper highlights methodologies to effectively utilize 5G for e-health use cases and its role to enable relevant digital services. It also provides a comprehensive discussion of the implementation issues, possible remedies and future research directions for 5G to alleviate the health challenges related to COVID-19.


id: cord-350646-7soxjnnk
title: Virtual reality for behavioral health workforce development in the era of COVID-19
author: Becker, Sara
date: 2020-10-09
txt: ./txt/cord-350646-7soxjnnk.txt
cache: ./cache/cord-350646-7soxjnnk.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0740547220304141
doi: 10.1016/j.jsat.2020.108157
summary:
  The coronavirus 2019 disease (COVID-19) pandemic emerged at a time of substantial investment in the United States substance use service infrastructure. SAMHSA charges TTCs with building the capacity of the behavioral health workforce to provide evidence-based interventions via locally and culturally responsive training and TA. This commentary describes how, in the wake of the COVID-19 pandemic, TTCs rapidly adapted to ensure that the behavioral health workforce had continuous access to remote training and technical assistance. To ensure the modernization of the behavioral health service system, SAMHSA charges TTCs with building the capacity of the behavioral health workforce to provide evidence-based interventions via locally and culturally responsive training and TA (Katz, 2018) . TTCs are guided by extensive evidence that strategies beyond training are required for practice implementation and organizational change (Edmunds et al., 2013) , underscoring the critical need for virtual TA in the wake of the COVID-19 pandemic.

abstract:
  The coronavirus 2019 disease (COVID-19) pandemic emerged at a time of substantial investment in the United States substance use service infrastructure. A key component of this fiscal investment was funding for training and technical assistance (TA) from the Substance Abuse and Mental Health Services Administration (SAMHSA) to newly configured Technology Transfer Centers (TTCs), including the Addiction TTCs (ATTC Network), Prevention TTCs (PTTC Network), and the Mental Health TTCs (MHTTC Network). SAMHSA charges TTCs with building the capacity of the behavioral health workforce to provide evidence-based interventions via locally and culturally responsive training and TA. This commentary describes how, in the wake of the COVID-19 pandemic, TTCs rapidly adapted to ensure that the behavioral health workforce had continuous access to remote training and technical assistance. TTCs use a conceptual framework that differentiates among three types of technical assistance: basic, targeted, and intensive. We define each of these types of TA and provide case examples to describe novel strategies that the TTCs used to shift an entire continuum of capacity building activities to remote platforms. Examples of innovations include online listening sessions, virtual process walkthroughs, and remote “live” supervision. Ongoing evaluation is needed to determine whether virtual TA delivery is as effective as face-to-face delivery or whether a mix of virtual and face-to-face delivery is optimal. The TTCs will need to carefully balance the benefits and challenges associated with rapid virtualization of TA services to design the ideal hybrid delivery model following the pandemic.


id: cord-352049-68op3d8t
title: Model of epidemic control based on quarantine and message delivery
author: Wang, Xingyuan
date: 2016-09-15
url: https://doi.org/10.1016/j.physa.2016.04.009
doi: 10.1016/j.physa.2016.04.009
abstract:
  The model provides two novel strategies for the preventive control of epidemic diseases. One approach is related to the different isolating rates in latent period and invasion period. Experiments show that the increasing of isolating rates in invasion period, as long as over 0.5, contributes little to the preventing of epidemic; the improvement of isolation rate in latent period is key to control the disease spreading. Another is a specific mechanism of message delivering and forwarding. Information quality and information accumulating process are also considered there. Macroscopically, diseases are easy to control as long as the immune messages reach a certain quality. Individually, the accumulating messages bring people with certain immunity to the disease. Also, the model is performed on the classic complex networks like scale-free network and small-world network, and location-based social networks. Results show that the proposed measures demonstrate superior performance and significantly reduce the negative impact of epidemic disease.


id: cord-354783-2iqjjema
title: Containing misinformation spreading in temporal social networks
author: Wang, Wei
date: 2019-04-24
url: https://doi.org/10.1063/1.5114853
doi: 10.1063/1.5114853
abstract:
  Many researchers from a variety of fields including computer science, network science and mathematics have focused on how to contain the outbreaks of Internet misinformation that threaten social systems and undermine societal health. Most research on this topic treats the connections among individuals as static, but these connections change in time, and thus social networks are also temporal networks. Currently there is no theoretical approach to the problem of containing misinformation outbreaks in temporal networks. We thus propose a misinformation spreading model for temporal networks and describe it using a new theoretical approach. We propose a heuristic-containing (HC) strategy based on optimizing final outbreak size that outperforms simplified strategies such as those that are random-containing (RC) and targeted-containing (TC). We verify the effectiveness of our HC strategy on both artificial and real-world networks by performing extensive numerical simulations and theoretical analyses. We find that the HC strategy greatly increases the outbreak threshold and decreases the final outbreak threshold.


