Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 43 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 6606 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 55 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 42 node 11 network 4 Fig 3 model 2 protein 2 lymphoma 2 lymph 2 graph 2 ferret 2 drug 2 community 2 cervical 1 tree 1 time 1 temperature 1 target 1 tag 1 system 1 strategy 1 state 1 spleen 1 red 1 policy 1 peer 1 patient 1 parameter 1 p2p 1 measure 1 marrow 1 lymphoid 1 lymphocyte 1 lymphatic 1 lymphadenopathy 1 lymphadenitis 1 liposome 1 information 1 individual 1 host 1 gene 1 function 1 fog 1 energy 1 edge 1 dog 1 disorder 1 disease 1 datum 1 data 1 cpu 1 chapter Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 4229 node 2532 network 915 model 705 cell 688 time 653 disease 647 lymph 645 number 616 graph 504 datum 466 structure 464 system 425 degree 423 community 414 result 414 case 403 information 398 method 389 infection 387 algorithm 361 value 361 blood 352 edge 350 function 345 strategy 334 ferret 334 % 326 epidemic 315 lymphoma 304 process 299 probability 295 spleen 294 parameter 287 energy 283 v 283 type 274 lymphocyte 272 feature 260 drug 257 set 257 rate 256 size 255 individual 255 dynamic 254 packet 253 state 253 data 251 virus 247 distribution 246 study Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 401 Fig 220 al 191 et 190 j 179 k 139 • 138 . 136 T 121 RFID 120 Eq 106 S 104 Table 102 t 93 d 89 i 88 C 82 u 80 m 78 CPT 74 v 72 Node 70 SIR 68 SARS 67 N 65 M 64 f 64 K 63 E 60 Fog4Video 59 A 58 n 57 y 52 Conductance 50 IoT 50 EnRenew 48 L 47 D 45 Figure 44 VoD 43 B 42 WSN 42 Network 42 F 41 Networks 39 c 39 DC 38 − 38 PageRank 38 ADTRA 37 de Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 1489 we 982 it 541 i 280 they 103 them 95 one 38 itself 24 us 22 he 12 u 9 themselves 9 she 6 you 3 ourselves 3 me 2 's 1 theirs 1 s 1 getuserfromsubmittedjob_in_lsf 1 cord-014845-odnlt6fr Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 9376 be 1508 have 980 use 615 base 490 show 440 include 377 see 323 propose 321 follow 315 consider 303 give 270 cause 268 do 256 increase 217 reduce 216 compare 213 occur 213 infect 210 provide 205 learn 204 represent 203 find 193 result 191 make 189 identify 188 spread 185 describe 182 select 180 perform 168 become 162 denote 159 take 157 set 152 affect 150 study 150 know 148 obtain 146 define 145 choose 142 contain 139 detect 139 connect 139 associate 135 present 134 decrease 129 target 129 receive 129 develop 123 require 119 generate Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 776 not 637 also 601 other 527 more 457 large 455 such 427 most 410 different 383 high 373 small 372 - 346 only 343 then 333 random 278 well 277 however 262 same 257 first 252 important 241 low 234 many 229 new 221 e.g. 197 complex 196 infectious 191 splenic 191 often 187 social 187 initial 185 red 182 lymphoid 175 thus 175 infected 171 multiple 167 as 166 common 162 lymphatic 162 average 159 susceptible 157 local 155 less 153 similar 144 cervical 142 specific 141 therefore 138 acute 136 so 134 usually 132 respectively 126 good Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 111 most 75 good 48 large 43 least 39 Most 36 high 31 short 22 small 12 near 10 simple 9 low 8 late 7 strong 7 bad 6 great 6 early 5 big 3 farth 3 cool 3 close 2 old 2 new 2 long 2 fast 2 Least 1 weak 1 smooth 1 p(t 1 myeloblast 1 mild 1 lev_fac 1 leftmost 1 hot 1 fit 1 fair 1 easy 1 dense 1 data_t 1 broad 1 ImageNet 1 CoV-2-host Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 316 most 22 least 9 well 1 worst 1 finest 1 fast 1 -so Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 6 github.com 2 bitbucket.org 1 www.hamsterster.com 1 trang1618.github.io 1 knetminer.org 1 en.wikipedia.org 1 cran.r-project.org 1 coronavirus.jhu.edu Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 3 http://github.com/hackl/tikz-network 1 http://www.hamsterster.com 1 http://trang1618.github.io/treeheatr 1 http://knetminer.org 1 http://github.com/pholme/exact-importance 1 http://github.com/jadbin/SLGAT 1 http://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy 1 http://en.wikipedia.org/wiki/Simplicial_complex.] 1 http://cran.r-project.org/package=treeheatr 1 http://coronavirus.jhu.edu/map.html 1 http://bitbucket.org/p2pElearning/icls/src/ 1 http://bitbucket.org/p2pElearning/distro1/src/ Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 2 holme@cns.pi.titech.ac.jp 1 ttle@pennmedicine.upenn.edu 1 k.blyuss@sussex.ac.uk Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 8 nodes are usually 6 nodes are not 5 networks identifying influential 5 node is important 4 nodes are able 4 nodes are more 4 nodes is equal 4 nodes were not 4 results are promising 3 disease is usually 3 information is available 3 model is not 3 network becomes more 3 network is growth 3 network is not 3 networks is always 3 networks is dependent 3 networks is growth 3 nodes are most 3 nodes do not 3 nodes have different 3 nodes have high 3 nodes is smaller 2 algorithm is not 2 cells are not 2 cells are often 2 cells are round 2 cells are small 2 communities are more 2 community is faster 2 diseases are also 2 edge are β 2 edges are then 2 edges represent friendship 2 ferret included splenomegaly 2 ferrets are similar 2 ferrets did not 2 ferrets has not 2 ferrets using ultrasound 2 ferrets were not 2 information based behavioral 2 method is always 2 method is usually 2 model is then 2 network is very 2 network using tikz 2 networks do not 2 node are triangles 2 node has not 2 node is also Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 2 network is not growth 2 nodes are not necessarily 1 algorithm is not suitable 1 cases are not normally 1 cells are not limited 1 community is not stable 1 degree has no parent 1 edge does not really 1 ferrets had no clinical 1 function is not clear 1 information was not available 1 methods is not very 1 model are not absolutely 1 model is not dynamic 1 network are not very 1 network has no pockets 1 network is not fully 1 node has no virus 1 nodes are not closely 1 nodes are not radiographically 1 nodes do not even 1 nodes do not ever 1 nodes is not frequently 1 nodes were not distinguishable 1 nodes were not ultrasonographically 1 system is not present 1 systems are not sufficiently A rudimentary bibliography -------------------------- id = cord-034545-onj7zpi1 author = Abuelkhail, Abdulrahman title = Internet of things for healthcare monitoring applications based on RFID clustering scheme date = 2020-11-03 keywords = RFID; datum; node; tag summary = The mathematical model optimizes the following objective functions: (1) minimizing the total distance between CHs and CMs to improve positioning accuracy; and (2) minimizing the number of clusters which reduces the signal transmission traffic Feature 6 (F-6): two level security is obtained by when a node writes data to its RFID tag, the data is signed with a signature, which is a hash value, the obtained hash is encrypted with a AES 128 bits shared key doi = 10.1007/s11276-020-02482-1 id = cord-196353-p05a8zjy author = Backhausz, ''Agnes title = Virus spread and voter model on random graphs with multiple type nodes date = 2020-02-17 keywords = graph; model; node summary = The two processes can be connected by the following idea: We can see virus spread as a special case of the voter model with two different opinions (healthy and infected), but only one of the opinions (infected) can be transmitted, while any individuals with infected opinion switch to healthy opinion after a period of time. We examined the virus spread with vaccination and the voter model on random graphs of different structures, where in some cases the nodes of the graph corresponding to the individuals of the network are divided into groups representing significantly distinct properties for the process. To study the result on the discretized model, we generated 5 random graphs with N = 10000 nodes for each graph structure, and run the process 20 times on a random graph with independent In case of most of the structures we can derive rather different outcomes on the same graph with different initial values concerning the peak of the virus. doi = nan id = cord-024499-14jlk5tv author = Balalau, Oana title = SubRank: Subgraph Embeddings via a Subgraph Proximity Measure date = 2020-04-17 keywords = node summary = We show that our subgraph embeddings are comprehensive and achieve competitive performance on three important data mining tasks: community detection, link prediction, and cascade growth prediction. A cascade graph is sampled for a set of random walks, which are given as input to a gated neural network to predict the future size of the cascade. In [3] , the authors propose an inductive framework for computing graph embeddings, based on training an attention network to predict a graph proximity measure, such as graph edit distance. Given a graph G = (V, E) and set of subgraphs of G, S = {S 1 , S 2 , · · · , S k }, we learn their representations as dense vectors, i.e. as embeddings. doi = 10.1007/978-3-030-47426-3_38 id = cord-276178-0hrs1w7r author = Bangotra, Deep Kumar title = An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare date = 2020-07-13 keywords = WSN; energy; network; node summary = 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 problem of energy efficiency during the routing of data packets from source to target in case of IoToriented WSN is significantly addressed by another network-based routing protocol known as GreeDi [6] . The problem of energy efficiency during the routing of data packets from source to target in case of IoT-oriented WSN is significantly addressed by another network-based routing protocol known as GreeDi [6] . The proposed method of relay node selection using IOP could be understood by considering an example of WSN shown in Figure 2 and using the naïve Baye''s algorithm on the generic data available in Table 4 , to find the optimal path in terms of energy efficiency and reliability from source node S to destination node D. doi = 10.3390/s20143887 id = cord-285350-64mzmiv3 author = Bhagatkar, Nikita title = An integrated P2P framework for E-learning date = 2020-06-29 keywords = Bruijn; node; p2p; peer summary = The focus of this paper is to design and develop a Peer-to-Peer Presentation System (P2P-PS) that supports E-learning through live media streaming coupled with a P2P shared whiteboard. In current situation of pandemic outbreak of COVID-19, our P2P-PS may, in fact, be an ideal complementary system for offloading pressure on Zoom on WebEx. To organize the stored contents, we used an efficient implementation of Distributed Hash Table ( DHT) based on de Bruijn graphs. -It incorporates an efficient DHT-based sharing and searching of media and document files, which is implemented using de Bruijn graph-based overlays. We use a de Bruijn graph-based overlay for implementing distributed hash table (DHT) organization for stored Fig. 2 Overall system architecture of P2P-PS materials. The bootstrap server randomly selects a set of required number of active peers from the entire active peer list and returns the same to the requesting node. doi = 10.1007/s12083-020-00919-0 id = cord-022561-rv5j1201 author = Boes, Katie M. title = Bone Marrow, Blood Cells, and the Lymphoid/Lymphatic System date = 2017-02-17 keywords = EMH; Fig; anemia; animal; blood; cause; cell; chapter; disease; disorder; dog; lymph; lymphocyte; lymphoid; lymphoma; marrow; node; red; spleen summary = Mechanisms contributing to glucocorticoid-mediated neutrophilia include the following: • Increased release of mature neutrophils from the bone marrow storage pool • Decreased margination of neutrophils within the vasculature, with a resulting increase in the circulating pool • Decreased migration of neutrophils from the bloodstream into tissues The magnitude of neutrophilia tends to be species dependent, with dogs having the most pronounced response (up to 35,000 cells/µL) and in decreasing order of responsiveness, cats (30,000 cells/µL), horses (20,000 cells/µL), and cattle (15,000 cells/µL) having less marked responses. As a result, animals with Chédiak-Higashi 746.e1 CHAPTER 13 Bone Marrow, Blood Cells, and the Lymphoid/Lymphatic System von Willebrand disease (vWD) is the most common canine hereditary bleeding disorder and has also been described in many other domestic species. doi = 10.1016/b978-0-323-35775-3.00013-8 id = cord-125089-1lfmqzmc author = Chandrasekhar, Arun G. title = Interacting Regional Policies in Containing a Disease date = 2020-08-24 keywords = node; policy summary = We show that a regional quarantine policy''s effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are proactive: triggering quarantines in reaction to neighbors'' infection rates, in some cases even before infections are detected internally. In the SI (Theorem 1) we prove that, with no delays in detection and no leakage, a (k, x)regional policy halts infection among all nodes beyond distance k +1 from i 0 with probability approaching 1 (as the population grows) if and only if the network satisfies growth-balance. Given that real-world networks have short average distances between nodes [32], non-trivial delays in detection allow the disease to escape a regional quarantine. doi = nan id = cord-028660-hi35xvni author = Chen, Jie title = Three-Way Decisions Community Detection Model Based on Weighted Graph Representation date = 2020-06-10 keywords = community; node summary = Community detection algorithm based on three-way decisions (TWD) forms a multi-layered community structure by hierarchical clustering and then selects a suitable layer as the community detection result. Therefore, in this paper, we propose a method for three-way decisions community detection based on weighted graph representation (WGR-TWD). In this paper, we propose a three-way decisions community detection model based on weighted graph representation (WGR-TWD). The graph representation can well transform the global structure of the network into vector representation and make the two nodes in the boundary region that appear in the same community more similar by using the weight. (1) We use weighted graph representation to obtain the global structure information of the network to guide the processing of the boundary region, which gets a better three-way decisions community detection method. In this paper, we propose a method for three-way decisions community detection based on weighted graph representation. doi = 10.1007/978-3-030-52705-1_11 id = cord-027451-ztx9fsbg author = De Chiara, Davide title = Data Mining for Big Dataset-Related Thermal Analysis of High Performance Computing (HPC) Data Center date = 2020-05-25 keywords = cpu; node; temperature summary = This work presents an algorithm that clusters hotspots with the goal of reducing a data centre''s large thermal-gradient due to uneven distribution of server dissipated waste heat followed by increasing cooling effectiveness. Thermal-aware schedulers adopt different thermal-aware approaches (e.g. system-level for work placements [16] ; execute ''hot'' jobs on ''cold'' compute nodes; predictive model for job schedule selection [17] ; ranked node queue based on thermal characteristics of rack layouts and optimisation (e.g. optimal setpoints for workload distribution and supply temperature of the cooling system). Analysis conducted are as follows: hotspots localisation; users categorisations based on submitted jobs to CRESCO6 cluster; compute nodes categorisation based on thermal behaviour of internal and surrounding air temperatures due to workload related waste heat dissipation. Data collected are related to: relevant parameters for each node (e.g. inlet air temperature, internal temperature of each node, energy consumption of CPU, RAM, memory, etc…); environmental parameters (e.g. air temperatures and humidity in both the hot and cold aisles); cooling system related parameters (e.g. fan speed); and finally, individual users who submit their jobs to cluster node. doi = 10.1007/978-3-030-50436-6_27 id = cord-017590-w5copp1z author = Fresnadillo, María J. title = A SIS Epidemiological Model Based on Cellular Automata on Graphs date = 2009 keywords = individual; node summary = The main goal of this work is to introduce a new SIS epidemic model based on a particular type of finite state machines called cellular automata on graphs. The state of each cell stands for the fraction of the susceptible and infected individuals of the cell at a particular time step and the evolution of these classes is given in terms of a local transition function. The model introduced in this paper deals with SIS epidemic diseases (for example the group of those responsible for the common cold), that is, the population is divided into susceptible individuals (S) and infected individuals (I). The main goal of this work is to introduce a new SIS model to simulate the spread of a general epidemic based on cellular automata on graph. Nevertheless, in this paper we will consider a more efficient topology to model an epidemic disease, which is given by an undirected graph where its nodes stand for the cells of the cellular automata. doi = 10.1007/978-3-642-02481-8_160 id = cord-005090-l676wo9t author = Gao, Chao title = Network immunization and virus propagation in email networks: experimental evaluation and analysis date = 2010-07-14 keywords = Fig; network; node; strategy 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. doi = 10.1007/s10115-010-0321-0 id = cord-322746-28igib4l author = Gosche, John R. title = Acute, subacute, and chronic cervical lymphadenitis in children date = 2007-06-06 keywords = cervical; lymphadenitis; node; patient summary = Involvement of superficial or deep cervical lymph nodes is also frequently indicative of the site of entry since superficial nodal enlargement usually reflects invasion through an epithelial surface (eg, buccal mucosa, skin, scalp), whereas deep nodal enlargement results from an infectious process involving more central structures (eg, middle ear, posterior pharynx). Finally, serologic testing for human immunodeficiency virus (HIV) should be considered in any patient with at-risk behaviors, generalized lymphadenitis, and unusual or recurrent infections caused by opportunistic organisms. Plain radiographs are seldom necessary in patients with acute cervical lymphadenitis, but may occasionally document the primary site of an infection (eg, pneumonia, sinusitis, or dental caries). 8 Acute viral associated cervical lymphadenitis typically develops following an upper respiratory tract infection. 11 Bilateral, acute cervical lymphadenitis associated with a viral upper respiratory tract infection rarely requires additional diagnostic testing or specific therapy. doi = 10.1053/j.sempedsurg.2006.02.007 id = cord-034824-eelqmzdx author = Guo, Chungu title = Influential Nodes Identification in Complex Networks via Information Entropy date = 2020-02-21 keywords = SIR; network; node 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. doi = 10.3390/e22020242 id = cord-102935-cx3elpb8 author = Hassani-Pak, Keywan title = KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species date = 2020-04-24 keywords = gene; node summary = Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. Even when 38 the task of gathering information is complete, it is demanding to assemble a coherent view of how 39 each piece of evidence might come together to "tell a story" about the biology that can explain how 40 multiple genes might be implicated in a complex trait or disease. doi = 10.1101/2020.04.02.017004 id = cord-010727-fiukemh3 author = Holme, Petter title = Three faces of node importance in network epidemiology: Exact results for small graphs date = 2017-12-05 keywords = Fig; node summary = We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). [13] is a bit different from ours-they call important nodes for vaccination "blockers" and important nodes for influence maximization "spreaders".) * holme@cns.pi.titech.ac.jp We will proceed by discussing our setup in greater detail: our implementation of the SIR model, how to analyze the three aspects of importance, network centrality measures that we need for our analysis, and our results, including the smallest networks where different nodes come out as most important. This is the smallest graph where the most important single node (n = 1) is different for influence maximization, vaccination, and sentinel surveillance. doi = 10.1103/physreve.96.062305 id = cord-010751-fgk05n3z author = Holme, Petter title = Objective measures for sentinel surveillance in network epidemiology date = 2018-08-15 keywords = measure; network; node 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. doi = 10.1103/physreve.98.022313 id = cord-319291-6l688krc author = Hung, Chun-Min title = Alignment using genetic programming with causal trees for identification of protein functions date = 2006-09-01 keywords = function; model; node; protein summary = Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. The hybrid model, namely Alignment using Genetic programming with Causal Tree (AGCT), is a heuristic evolutionary method that contains three basic components: (i) genetic programming with innerexchanged individual strategy, (ii) causal trees [4, 28, 31] with probabilistic reasoning, and (iii) construction of hierarchical homologies with local block-to-block alignment using the methods of moment invariant and robust points matching (RPM) [24] . doi = 10.1016/j.na.2005.09.048 id = cord-155475-is3su3ga author = Kalogeratos, Argyris title = Winning the competition: enhancing counter-contagion in SIS-like epidemic processes date = 2020-06-24 keywords = node; state summary = Motivated by social epidemics, we apply this method to a generic continuous-time SIS-like diffusion model where we allow for: i) arbitrary node transition rate functions that describe the dynamics of propagation depending on the network state, and ii) competition between the healthy (positive) and infected (negative) states, which are both diffusive at the same time, yet mutually exclusive on each node. Among them, the Largest Reduction in Infectious Edges (LRIE) [9] results to be the optimal greedy algorithm for resource allocation under limitations in the resource budget, in the N -intertwined Susceptible-Infected-Susceptible (SIS) epidemic model. In this study, we propose the Generalized Largest Reduction in Infectious Edges (gLRIE) strategy, which is adapted for the diffusion competition of recurrent epidemics, as well as nonlinearity and saturation of the functions of node transition rates. doi = nan id = cord-102588-vpu5w9wh author = Le, Trang T. title = treeheatr: an R package for interpretable decision tree visualizations date = 2020-07-10 keywords = node; tree summary = Summary treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree''s leaf nodes. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students'' understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. We have developed the treeheatr package to incorporate the functionality of ggparty but also utilize the leaf node space to display the data as a heatmap, a popular visualization that uncovers groups of samples and features in a dataset (Wilkinson and Friendly, 2009, Galili,T. The following lines of code compute and visualize the conditional decision tree along with the heatmap containing features that are important for constructing this model ( Fig. 1) : When the first argument x is a data.frame object representing the dataset instead of the decision tree, treeheatr automatically computes a conditional tree with default parameters for visualization. doi = 10.1101/2020.07.10.196352 id = cord-248848-p7jv79ae author = Lee, Kookjin title = Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems date = 2020-10-28 keywords = node; parameter summary = We emphasize that our approach is closely related to [63] , where neural networks are trained to approximate the action of the first-order time-integration scheme applied to latent dynamics and, at each time step, the neural networks take a set of problem-specific parameters as well as reduced state as an input. PNODEs still can learn multiple trajectories, which are characterized by the ODE parameters, even if the same initial states are given for different ODE parameters, which is not achievable with NODEs. Furthermore, the proposed framework is significantly simpler than the common neural network settings for NODEs when they are used to learn latent dynamics: the sequence-to-sequence architectures as in [9, 61, 74, 45, 46] , which require that a (part of) sequence is fed into the encoder network to produce a context vector, which is then fed into the NODE decoder network as an initial condition. doi = nan id = cord-026306-mkmrninv author = Lepskiy, Alexander title = Belief Functions for the Importance Assessment in Multiplex Networks date = 2020-05-15 keywords = node summary = The measures use the combination of "high", "low" and "(high, low)" probabilities of the influence based on weighted and unweighted degrees of nodes via Dempster''s rule. Secondly, one can aggregate connections between pairs of nodes to obtain monoplex network and then apply centrality measures to a new weighted graph. In this Section we describe a graph model with one layer of interaction as well as the construction of centrality measure based on a mass function for a network. Additionally, if we consider a 1-neighborhood of nodes in multiplex acyclic graphs with two layers then the following propositions concerning the aggregated interaction centrality value can be proved. We apply Dempster-Shafer theory in order to reveal key elements in undirected weighted graphs as well as to aggregate interactions between nodes into the total ranking. If nodes cooperate with each other on different levels of interactions then we apply a combination rule to mass functions obtained for different layers of a multiplex structure. doi = 10.1007/978-3-030-50143-3_22 id = cord-328875-fgeudou6 author = Leung, Alexander K. C. title = Cervical lymphadenitis: Etiology, diagnosis, and management date = 2009-04-18 keywords = cervical; lymphadenopathy; node summary = Acute bilateral cervical lymphadenitis is usually caused by a viral upper respiratory tract infection or streptococcal pharyngitis. Generalized lymphadenopathy is often caused by a viral infection, and less frequently by malignancies, collagen vascular diseases, and medications. Offending organisms usually fi rst infect the upper respiratory tract, anterior nares, oral cavity, or skin in the head and neck area before spreading to the cervical lymph nodes. Bartonella henselae (cat-scratch disease), nontuberculosis mycobacteria (eg, Mycobacterium avium-intracellulare, Mycobacterium scrofulaceum ), and Mycobacterium tuberculosis ("scrofula") are important causes of subacute or chronic cervical lymphadenopathy [ 8 ] . Kikuchi-Fujimoto disease (histocytic necrotizing lymphadenitis) is a benign cause of lymph node enlargement, usually in the posterior cervical triangle [ 9 ] . Acute bilateral cervical lymphadenitis is usually caused by a viral upper respiratory tract infection or pharyngitis due to S. doi = 10.1007/s11908-009-0028-0 id = cord-028688-5uzl1jpu author = Li, Peisen title = Multi-granularity Complex Network Representation Learning date = 2020-06-10 keywords = information; network; node 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: doi = 10.1007/978-3-030-52705-1_18 id = cord-024504-p2vxnn9z author = Lyu, Tianshu title = Node Conductance: A Scalable Node Centrality Measure on Big Networks date = 2020-04-17 keywords = Conductance; Node; centrality summary = Moreover, with the help of node embedding techniques, Node Conductance is able to be approximately calculated on big networks effectively and efficiently. Thorough experiments present the differences between existing centralities and Node Conductance, its outstanding ability of detecting influential nodes on both static and dynamic network, and its superior efficiency compared with other global centralities. Random walk, which Node Conductance is based on, is also an effective sampling strategy to capture node neighborhood in the recent network embedding studies [10, 21] . We visualize the special case, football network, in order to have an intuitive sense of the properties of Degree, Betweenness, and Node Conductance (other centralities are presented in the Supplementary Material). Comparing with the existing global centralities, Node Conductance computed by DeepWalk is much more scalable and capable to be performed on big datasets. We also rethink the widely used network representation model, DeepWalk, and calculate Node Conductance approximately by the dot product of the input and output vectors. doi = 10.1007/978-3-030-47436-2_40 id = cord-269711-tw5armh8 author = Ma, Junling title = The importance of contact network topology for the success of vaccination strategies date = 2013-05-21 keywords = Table; network; node 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). doi = 10.1016/j.jtbi.2013.01.006 id = cord-306727-2c1m04je author = Pandey, Prateek title = Promoting Trustless Computation Through Blockchain Technology date = 2020-05-20 keywords = Blockchain; node summary = The paper explains the blockchain technology and a variety of its implementation through five different use cases in the field of drug supply chain, health insurance, land record management, courier services, and immigration records. Therefore, virtually all the use cases, from money transaction to mushroom supply chain, and from agricultural commodity market place to electronic health records (EHR) are a potential client for blockchain technology. In this article, the authors propose the use of Blockchain in five different use cases, namely drug supply chain, health insurance, land record management, courier services, and immigration records. Therefore, Drug supply, health insurance, and land records blockchains are implemented over the Ethereum network, whereas immigration and courier blockchains are implemented on hyper ledger fabric. While implementing the private Blockchain for immigration records and courier tracking, we observe the performance of the built network in terms of throughput, which is the number of transactions written in the ledger per second. doi = 10.1007/s40009-020-00978-0 id = cord-168862-3tj63eve author = Porter, Mason A. title = Nonlinearity + Networks: A 2020 Vision date = 2019-11-09 keywords = Kuramoto; edge; model; network; node; time 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] . doi = nan id = cord-199630-2lmwnfda author = Ray, Sumanta title = Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs date = 2020-07-05 keywords = SARS; drug; host; node; protein summary = Therefore, host-(1) We link existing high-quality, long-term curated and refined, large scale drug/protein -protein interaction data with (2) molecular interaction data on SARS-CoV-2 itself, raised only a handful of weeks ago, (3) exploit the resulting overarching network using most advanced, AI boosted techniques (4) for repurposing drugs in the fight against SARS-CoV-2 (5) in the frame of HDT based strategies. As for (3)-(5), we will highlight interactions between SARS-Cov-2-host protein and human proteins important for the virus to persist using most advanced deep learning techniques that cater to exploiting network data. As per our simulation study, a large fraction, if not the vast majority of the predictions establish true, hence actionable interactions between drugs on the one hand and SARS-CoV-2 associated human proteins (hence of use in HDT) on the other hand. doi = nan id = cord-119522-2ua8218z author = Reddy, C. Rajashekar title = Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors date = 2020-05-12 keywords = PM2.5; node summary = This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes in Indian urban conditions. For spatial interpolation, inverse distance weighing (IDW) scheme is used on these nodes for the data collected before and during the bursting of firecrackers on the main night of Diwali (one of the most popular festivals in India) to show the variability pattern in a small campus, hot spot detection and need for a dense deployment to provide better local pollution indicators. doi = nan id = cord-000196-lkoyrv3s author = Salathé, Marcel title = Dynamics and Control of Diseases in Networks with Community Structure date = 2010-04-08 keywords = CBF; network; node 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. doi = 10.1371/journal.pcbi.1000736 id = cord-349724-yq4dphmb author = Santos, Hugo title = A Multi-Tier Fog Content Orchestrator Mechanism with Quality of Experience Support date = 2020-05-06 keywords = Fog4Video; Unit; fog; node summary = For the analysis phase, the Fog4Video collects information about available bandwidth, delay, stall duration, number of stall events, and monetary cost from the network, user client, and fog node. Based on our analysis of the state-of-the-art, we conclude that VoD services deployed in a multi-tier fog architecture improve the QoE by efficiently orchestrating fog nodes resources, while reducing delay and amount of data uploading/downloading to the cloud in a cost efficient fashion. Based on such architecture, Fog4Video classifies the connectivity and resources of each available fog node into a multi-criteria rank, where it considers the AHP method to assign different degrees of importance for each criterion to provide better QoE for each user. Based on these values, Fog4Video implemented in the Orchestrator considers network, fog node, and user information to perform real-time content orchestration, i.e., it chooses an appropriate Streaming Unit from a given tier for the client to download the video. doi = 10.1016/j.comnet.2020.107288 id = cord-020885-f667icyt author = Sharma, Ujjwal title = Semantic Path-Based Learning for Review Volume Prediction date = 2020-03-17 keywords = graph; network; node summary = 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. In this work, -We propose a novel method that incorporates restaurants and their attributes into a multimodal graph and extracts multiple, bimodal low dimensional representations for restaurants based on available paths through shared visual, textual, geographical and categorical features. In this section, we discuss prior work that leverages graph-based structures for extracting information from multiple modalities, focussing on the auto-captioning task that introduced such methods. For each of these sub-networks, we perform random walks and use a variant of the heterogeneous skip-gram objective introduced in [6] to generate low-dimensional bimodal embeddings. Our attention-based model combines separately learned bimodal embeddings using a late-fusion setup for predicting the review volume of the restaurants. doi = 10.1007/978-3-030-45439-5_54 id = cord-010739-28qfmj9x author = Sherborne, N. title = Bursting endemic bubbles in an adaptive network date = 2018-04-09 keywords = Fig; node summary = Adaptive network models that account for both epidemic and behavioral change have found oscillations, but in an extremely narrow region of the parameter space, which contrasts with intuition and available data. In this paper we propose a simple susceptible-infected-susceptible epidemic model on an adaptive network with time-delayed rewiring, and show that oscillatory solutions are now present in a wide region of the parameter space. [13] showed that cutting links between susceptible and infected individuals can lead to epidemic reemergence, with long periods of low disease prevalence punctuated by large outbreaks. Figure 2 (d) illustrates this behavior both in simulation and in the meanfield model (4) , showing how after the initial outbreak each new wave of infection is preceded by the recovery of network connectivity. Furthermore, since rewiring nodes choose their new neighbors uniformly at random from all available susceptible nodes, the initial network topology itself is transient, as shown in Fig. 4 , and, as a result, over time our model becomes more relevant. doi = 10.1103/physreve.97.042306 id = cord-017062-dkw2sugl author = Singh, Indu title = Delivery Systems for Lymphatic Targeting date = 2013-10-08 keywords = drug; liposome; lymphatic; node; system; target summary = The major purpose of lymphatic targeting is to provide an effective anticancer chemotherapy to prevent the metastasis of cancer cells by accumulating the drug in the regional lymph node. Hydrophilic multiwalled carbon nanotubes (MWNTs) coated with magnetic nanoparticles (MN-MWNTs) have emerged as an effective delivery system for lymphatic targeting following subcutaneous injection of these particles into the left footpad of Sprague Dawley rats; the left popliteal lymph nodes were dyed black. In vivo study, about eight times lymph node uptake of LyP-1-NPs was seen in metastasis than that of NPs, indicated LyP-1-NP as a promising carrier for targetspecifi c drug delivery to lymphatic metastatic tumours [ 129 ] . To improve carrier retention in lymph nodes, a new method of increasing lymphatic uptake of subcutaneously injected liposome utilises the high-affi nity ligands biotin and avidin. doi = 10.1007/978-1-4614-9434-8_20 id = cord-284186-zf1w8ksm author = Suran, J. N. title = Radiographic and ultrasonographic findings of the spleen and abdominal lymph nodes in healthy domestic ferrets date = 2017-04-17 keywords = ferret; lymph; node summary = The goal of this prospective, cross-sectional study was to describe the characteristics of the spleen and abdominal lymph nodes on radiographs and with ultrasound in a sample of clientowned, clinically healthy domestic ferrets ( Mustela putorius furo ). For the calculation of sample sizes, the standard deviations of previously reported measurements of abdominal viscera in clinically healthy ferrets were compared, including gross renal measurements (1·25, 1·5 mm), ultrasonographic adrenal thickness (0·5, 0·6 mm) and ultrasonographic jejunal lymph node thickness (1·39, 2.0 mm) (O '' Brien et al . The results presented in this study provide the most comprehensive evaluation of the spleen and abdominal lymph nodes with radiographs and ultrasound in clinically healthy ferrets to date. Based on the results of this study, the jejunal, pancreaticoduodenal, hepatic and caudal mesenteric lymph nodes can be routinely detected with ultrasound in most ferrets. doi = 10.1111/jsap.12680 id = cord-322890-w78tftva author = Suran, Jantra Ngosuwan title = IMAGING FINDINGS IN 14 DOMESTIC FERRETS (MUSTELA PUTORIUS FURO) WITH LYMPHOMA date = 2013-06-06 keywords = MRI; ferret; lymphoma; node summary = Radiographs, CT, MRI, static ultrasound images, and when available, ultrasound cine loops were retrospectively evaluated, and abnormal findings were recorded by J.N.S. The imaging reports generated by a board-certified veterinary radiologist at the time the study was performed were also reviewed. Cytology of the right renal mass in the first ferret was not performed; however, the patient received chemotherapy for the treatment of lymphoma, confirmed from aspiration and biopsy of an enlarged lymph node, and the mass was seen to decrease in size during follow-up studies (Fig. 5) . Concurrent abdominal imaging findings considered incidental to the diagnosed lymphoma included renal cysts (8) , cystic lymph nodes (7), adrenomegaly in ferrets with diagnosed adrenal disease (5), and pancreatic nodules in ferrets diagnosed with insulinoma (2). Additional radiographic findings in this ferret included splenomegaly, hepatomegaly, and abdominal masses consistent with enlarged lymph nodes. doi = 10.1111/vru.12068 id = cord-027178-tqj8jgem author = Tian, Changbo title = Modeling of Anti-tracking Network Based on Convex-Polytope Topology date = 2020-06-15 keywords = CPT; node summary = From the aspects of network stability, network resilience and destroy-resistance, we propose the convex-polytope topology (CPT) applied in the anti-tracking network. From the experimental results, CPT has better robustness, resilience and destroy-resistance confronted with dynamically changed topology, and performs better in the efficiency of network self-optimization. Especially, for the P2P-based anti-tracking network which takes the advantage of the wide distribution of nodes for tracking-resistant communication, it is important to keep a stable, secure and destroy-resistant topology structure. But the convex-polytope structure still has some extreme cases shown in Fig. 2 which may pose a potential threat on the structure stability and communication efficiency of anti-tracking network because of the key nodes. According to the properties of CPT, we can achieve the self-optimization of anti-tracking network to balance the distribution of nodes'' connectivity. For each network constructed by CPT, NN and DHT, we randomly remove p percent of nodes and count the time requirement of network self-optimization as the metric. doi = 10.1007/978-3-030-50417-5_32 id = cord-016196-ub4mgqxb author = Wang, Cheng title = Study on Efficient Complex Network Model date = 2012-11-20 keywords = network; node 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. doi = 10.1007/978-3-642-35398-7_20 id = cord-024437-r5wnz7rq author = Wang, Yubin title = SLGAT: Soft Labels Guided Graph Attention Networks date = 2020-04-17 keywords = SLGAT; node summary = In this paper, we propose a soft labels guided graph attention network (SLGAT) to improve the performance of node representation learning by leveraging generated pseudo labels. Graph attention networks (GAT) [23] , which is one of the most representative GCNs, learns the weights for neighborhood aggregation via self-attention mechanism [22] and achieves promising performance on semi-supervised node classification problem. In this paper, we propose soft labels guided attention networks (SLGAT) for semi-supervised node representation learning. First, SLGAT aggregates the features of neighbors using convolutional networks and predicts soft labels for each node based on the learned embeddings. The weights for neighborhood aggregation learned by a feedforward neural network based on both label information of central nodes and features of neighboring nodes, which can lead to learning more discriminative node representations for classification. Unlike the prior graph attention networks [23, 28] , we use label information as guidance to learn the weights of neighboring nodes for feature aggregation. doi = 10.1007/978-3-030-47426-3_40 id = cord-014845-odnlt6fr author = Wu, Jia title = Reducing Energy Consumption and Overhead Based on Mobile Health in Big Data Opportunistic Networks date = 2016-08-13 keywords = ADTRA; data; node summary = title: Reducing Energy Consumption and Overhead Based on Mobile Health in Big Data Opportunistic Networks This algorithm can reduce energy consumption and overhead, then improve deliver ratio in big data communication. Compare with Spray and Wait algorithm, Binary spray and wait algorithm in opportunistic networks, this algorithm acquires good results by reduce energy consumption, overhead and deliver ratio. This algorithm can judge routing request and predict data data packets and then can overhead, energy consumption and deliver ratio in transmission. From what has been discussed above, ADTRA needs to solve energy consumption, overhead and deliver ratio when the algorithm is applied of big data environment. In wireless communication, if doctors or hospitals send all pictures to patients, many network resource must be wasted, especially in big data environment, mass of people join in transmission, there are no enough space storing effective information. doi = 10.1007/s11277-016-3610-4 id = cord-355393-ot7hztyk author = Yuan, Peiyan title = Community-based immunization in opportunistic social networks date = 2015-02-15 keywords = community; node summary = More interestingly, we find that high local importance but non-central nodes play a big role in epidemic spreading process, removing them improves the immunization efficiency by 25% to 150% at different scenarios. To this end, we investigate the evolution of community structure in opportunistic social networks, and analyze the effect of community-based immunization strategy on epidemic spreading. We observe that the most efficient immunization strategy on epidemic spreading is to remove nodes with high local importance in communities. Although many random mobility models, such as Random Walk and Random Way Point, have been widely used in opportunistic social networks for evaluating routing performance or even the epidemic dynamics [30, 31] , they cannot reflect the main features of human mobility, including the truncated power-law flights and pause-times, the heterogeneously bounded mobility areas of different nodes, etc. doi = 10.1016/j.physa.2014.10.087 id = cord-102394-vk4ag44m author = Zhang, Hai-Feng title = Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks date = 2016-08-14 keywords = node summary = The analytic results emph{quantitatively} describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Since the manner of the diffusion of awareness is quite different from the mechanism of epidemic spreading, the coupled disease-behavior interaction models in multiplex networks were also investigated [18] [19] [20] . Therefore, the SAUIR model is an irreversible process but the SAUIS model is a reversible process, which yields different theoretical methods to deal with their epidemic thresholds and the results. For our SAUIR model, on one hand, the epidemic threshold is very difficult or impossible to obtain by solving the meanfield based differential equations; on the other hand, in Ref. Near the epidemic threshold, the number of infected nodes is very few, indicating that the value of θ is a very small too. Then the epidemic thresholds for the improved SIS model and SIR model were obtained by using different theoretical methods. doi = 10.1209/0295-5075/114/38004 id = cord-007415-d57zqixs author = da Fontoura Costa, Luciano title = Correlations between structure and random walk dynamics in directed complex networks date = 2007-07-30 keywords = network; node 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. doi = 10.1063/1.2766683