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?' ---------------------------------------------------------- 113 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 6609 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 51 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- 109 network 17 model 13 Fig 11 node 6 individual 6 disease 6 datum 4 social 4 protein 4 figure 4 epidemic 4 community 4 author 4 Network 3 system 3 risk 3 neural 3 layer 3 image 3 gene 3 edge 3 Twitter 3 SARS 3 PPI 3 COVID-19 2 time 2 road 2 research 2 italian 2 interaction 2 innovation 2 friend 2 expression 2 entrepreneurial 2 drug 2 business 2 behavior 2 University 2 Social 2 SIR 1 vba 1 user 1 trial 1 transport 1 transmission 1 transaction 1 thing 1 tensor 1 target 1 symptom Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 11782 network 3310 node 3139 model 2242 datum 2037 time 1760 disease 1693 number 1652 system 1535 structure 1431 analysis 1385 information 1296 protein 1247 method 1226 interaction 1222 individual 1215 result 1159 epidemic 1148 study 1146 process 1112 approach 1096 community 1091 value 1048 case 1044 research 971 effect 964 degree 937 edge 928 infection 838 size 809 layer 808 dynamic 792 level 788 rate 769 algorithm 762 author 744 type 741 distribution 738 graph 733 example 727 contact 719 risk 703 behavior 697 % 692 population 692 gene 681 strategy 681 function 672 probability 657 state 651 performance Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 1586 al 1239 et 891 . 740 Fig 353 j 325 q 313 k 299 Network 278 SARS 276 COVID-19 275 Table 202 Networks 196 N 167 Health 165 Social 163 Figure 156 t 154 Eq 150 y 145 IoT 144 de 144 S 143 CoV-2 142 SIR 136 f 136 SIS 134 M 133 • 129 A 128 China 126 i 122 T 121 SR 118 d 118 US 111 University 109 ¼ 104 Twitter 101 C 100 sha 97 AI 96 PPI 94 homophily 94 EO 93 D 89 m 89 DOI 87 ML 87 L 84 Data Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 4651 we 2677 it 1036 they 1001 i 426 them 222 one 199 us 100 itself 96 he 81 you 71 themselves 18 she 8 ourselves 6 s 6 's 4 u 4 him 2 π 2 me 1 ߬ 1 ζ 1 u937 1 theirs 1 thee 1 rankðhaiÞ 1 id:1 1 himself 1 herself 1 her 1 d 1 cifar-10 Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 24874 be 4230 have 3291 use 1970 base 1492 show 973 do 867 include 843 consider 834 provide 834 give 749 make 745 propose 739 find 717 follow 703 represent 673 identify 654 see 606 spread 606 increase 602 compare 555 connect 520 take 507 generate 493 obtain 477 describe 463 infect 462 study 459 apply 454 learn 447 define 447 become 445 perform 440 predict 435 allow 434 relate 434 reduce 428 develop 425 lead 391 need 387 require 376 present 375 improve 371 understand 353 create 352 affect 348 know 344 determine 343 indicate 340 exist 340 contain Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 2286 not 2037 social 1711 also 1662 more 1643 such 1605 other 1389 different 1312 high 1297 - 1088 large 989 well 923 small 912 only 856 first 836 however 815 same 777 new 714 then 710 complex 707 most 702 human 675 random 673 low 648 neural 616 many 601 important 590 thus 546 as 529 real 489 very 459 global 452 infectious 449 several 438 specific 437 e.g. 434 here 430 similar 424 average 422 multiple 419 second 418 various 410 further 408 possible 407 therefore 394 infected 384 temporal 379 particular 366 susceptible 359 single 357 non Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 257 most 193 good 126 large 117 short 111 high 103 least 60 near 56 Most 36 low 30 small 24 close 19 simple 18 great 17 late 11 long 10 strong 9 early 9 bad 8 big 6 easy 6 Least 4 fast 3 outermost 3 hard 2 healthy 2 farth 2 busy 1 ‗patterns 1 wide 1 weak 1 w(x 1 tight 1 thin 1 subtle 1 slow 1 rich 1 old 1 new 1 n(t 1 hot 1 full 1 fit 1 few 1 eld 1 deep 1 deadly 1 clear 1 broad 1 ImageNet 1 D(p Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 450 most 63 least 31 well 2 fast 1 ‗best 1 worst 1 finest 1 -so 1 -drug Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 14 doi.org 8 github.com 4 www.kaggle.com 3 www.newsguardtech.com 3 www.biological-networks.org 3 creativecommons.org 2 www.isiknowledge.com 2 www.ebi.ac.uk 2 string-db.org 2 pubmed.cn 2 igraph.org 2 gephi.org 2 en.wikipedia.org 1 www.xinhu 1 www.worldpop.org 1 www.who 1 www.tkdl.res.in 1 www.synapse.org 1 www.sciensano.be 1 www.sccm.org 1 www.rcsb.org 1 www.protezionecivile.gov.it 1 www.hamsterster.com 1 www.example.com 1 www.dcc.fc.up.pt 1 www.dasmi.de 1 www.cytoscape.org 1 www.climatefish.org 1 www.cellcircuits.org 1 www.carmen.org.uk 1 www.flightstats.com 1 visone.info 1 string.embl.de 1 reggen 1 porthouston.com 1 pathology.unm.edu 1 osf.io 1 naver.com)." 1 mips.gsf.de 1 links.lww.com 1 journals.lww.com 1 interdom.i2r.a-star.edu.sg 1 imex.sf.net 1 github 1 elm.eu.org 1 dx.doi.org 1 dreamchallenges.org 1 domine.utdallas.edu 1 desktop.arcgis.com 1 data.4tu.nl Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 4 http://doi.org/10.1101/2020.05.05.20091736 3 http://github.com/hackl/tikz-network 3 http://creativecommons.org/licenses/by/4.0/ 2 http://www.newsguardtech.com/ 2 http://www.kaggle.com/ 2 http://www.isiknowledge.com/ 2 http://www.ebi.ac.uk/intact 2 http://www.biological-networks.org 2 http://string-db.org/ 2 http://pubmed.cn/ 2 http://github.com/ 2 http://gephi.org/ 1 http://www.xinhu 1 http://www.worldpop.org/ 1 http://www.who 1 http://www.tkdl.res.in 1 http://www.synapse.org 1 http://www.sciensano.be/en 1 http://www.sccm.org/Research/Research/Discovery-Research-Network/VIRUS-COVID-19-Registry 1 http://www.rcsb.org/ 1 http://www.protezionecivile.gov.it/ 1 http://www.newsguardtech.com/ratings/rating-process-criteria/ 1 http://www.kaggle.com/vtech6/ 1 http://www.kaggle.com/praveengovi/coronahack-chestxraydataset 1 http://www.hamsterster.com 1 http://www.example.com/index.html 1 http://www.dcc.fc.up.pt/gtries/ 1 http://www.dasmi.de 1 http://www.cytoscape.org 1 http://www.climatefish.org/index_en.htm 1 http://www.cellcircuits.org 1 http://www.carmen.org.uk 1 http://www.biological-networks.org/ 1 http://www.FlightStats.com 1 http://visone.info/ 1 http://string.embl.de 1 http://reggen 1 http://porthouston.com/wpcontent/uploads/COVID19_2020_03_18_BCT_BPT_Incident_Report.pdf 1 http://pathology.unm.edu/faculty/faculty/CVs/brian-hjelle 1 http://osf.io/jy5kz/ 1 http://naver.com)." 1 http://mips.gsf.de/genre/proj/dima2 1 http://links.lww.com/CCX/A163 1 http://journals.lww.com/ccejournal 1 http://interdom.i2r.a-star.edu.sg/ 1 http://imex.sf.net 1 http://igraph.org/r/ 1 http://igraph.org/c/ 1 http://github.com/reggenlab/ 1 http://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 1 zhang.nan152@zte.com.cn 1 yushan.siriwardhana@oulu.fi 1 ykim@asu.edu 1 vbha-tia@iiti.ac.in 1 sudhir.dixit@ieee.org 1 slpeng@hnu.edu.cn 1 singhalamit.iitd@gmail.com 1 pitta-cus@gmail.com 1 n.perra@greenwich.ac.uk 1 mjcg20@bath.ac.uk 1 mika.ylianttila@oulu.fi 1 madhusanka@ucd.ie 1 madhusanka.liyanage@oulu.fi 1 luciano@inatel.br 1 k.rabie@mmu.ac.uk 1 j.georgalakis@ids.ac.uk 1 holme@cns.pi.titech.ac.jp 1 harri.saarnisaari@oulu.fi 1 gueu@zhaw.ch 1 giordani@dei.unipd.it 1 divanov@hwr-berlin.de 1 chaoub.abdelaali@gmail.com 1 brejesh@ee.iitd.ac.in 1 andreas.hoepner@ucd.ie 1 alwalkey@bu.edu 1 adrian.kliks@put.poznan.pl 1 savvas.nicolaou@vch.ca Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 17 results are finally 11 network is not 10 approach is also 10 network is more 9 network did not 8 network does not 6 information is available 6 nodes are not 5 model does not 5 model is not 5 networks are not 5 networks do not 5 networks identifying influential 5 study are available 4 approach is not 4 community is more 4 effect is positive 4 effects were mainly 4 epidemic spreading process 4 individuals do not 4 model described above 4 models are not 4 network are not 4 network is also 4 network is important 4 network is much 4 network is subject 4 network is then 4 networks have also 4 networks is not 4 nodes are able 4 results are similar 4 studies have also 3 analysis is possible 3 approach does not 3 approach is applicable 3 case study method 3 effects do not 3 epidemic does not 3 individuals are more 3 methods do not 3 models are more 3 models did not 3 network has time 3 network is relatively 3 network is sparse 3 network is still 3 networks are often 3 networks are robust 3 networks are well Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 2 network are not all 2 nodes are not necessarily 2 systems are not robust 1 analysis is not able 1 analysis was not possible 1 approach is not applicable 1 approach is not integral 1 approach is not well 1 approaches are not robust 1 approaches is not specially 1 communities have no default 1 data including not only 1 edge does not really 1 edges were not random 1 effects do not simply 1 effects was not available 1 individual has no previous 1 individual is not immune 1 individuals has no impact 1 information are not consistent 1 interaction is not only 1 interactions were not entirely 1 methods does not properly 1 model has no altitude 1 model has no immunity 1 model is not general 1 model is not generic 1 model is not only 1 model is not suitable 1 models are not applicable 1 models are not explicitly 1 models are not expressive 1 models do not fully 1 network are not very 1 network did not significantly 1 network does not only 1 network does not significantly 1 network is no exception 1 network is not acceptable 1 network is not distinguishable 1 network is not fully 1 network is not independent 1 network is not invariable 1 network is not robust 1 network is not significant 1 network is not too 1 network is not very 1 networks are not always 1 networks are not immune 1 networks are not only A rudimentary bibliography -------------------------- id = cord-027304-a0vva8kb author = Achermann, Guillem title = An Information-Theoretic and Dissipative Systems Approach to the Study of Knowledge Diffusion and Emerging Complexity in Innovation Systems date = 2020-05-23 keywords = innovation; network; system 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. doi = 10.1007/978-3-030-50423-6_19 id = cord-025838-ed6itb9u author = Aljubairy, Abdulwahab title = SIoTPredict: A Framework for Predicting Relationships in the Social Internet of Things date = 2020-05-09 keywords = network; object; thing 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. doi = 10.1007/978-3-030-49435-3_7 id = cord-200147-ans8d3oa author = Arimond, Alexander title = Neural Networks and Value at Risk date = 2020-05-04 keywords = HMM; LSTM; network; neural; risk 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. doi = nan 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 = doi = 10.3390/s20143887 id = cord-203872-r3vb1m5p author = Baten, Raiyan Abdul title = Availability of demographic cues can negatively impact creativity in dynamic social networks date = 2020-07-12 keywords = alter; idea; network 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. doi = nan id = cord-350646-7soxjnnk author = Becker, Sara title = Virtual reality for behavioral health workforce development in the era of COVID-19 date = 2020-10-09 keywords = Network 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. doi = 10.1016/j.jsat.2020.108157 id = cord-285522-3gv6469y author = Bello-Orgaz, Gema title = Social big data: Recent achievements and new challenges date = 2015-08-28 keywords = Hadoop; Spark; Twitter; big; datum; network; social summary = doi = 10.1016/j.inffus.2015.08.005 id = cord-327401-om4f42os author = Bombelli, Alessandro title = Integrators'' global networks: A topology analysis with insights into the effect of the COVID-19 pandemic date = 2020-08-11 keywords = DHL; Fig; UPS; network; section 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. doi = 10.1016/j.jtrangeo.2020.102815 id = cord-027463-uc0j3fyi author = Brandi, Giuseppe title = A New Multilayer Network Construction via Tensor Learning date = 2020-05-25 keywords = network; tensor 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] . doi = 10.1007/978-3-030-50433-5_12 id = cord-340101-n9zqc1gm author = Bzdok, Danilo title = The Neurobiology of Social Distance date = 2020-06-03 keywords = Social; brain; effect; friend; human; loneliness; network; relationship 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] . doi = 10.1016/j.tics.2020.05.016 id = cord-186031-b1f9wtfn author = Caldarelli, Guido title = Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy date = 2020-10-05 keywords = Italy; Twitter; community; italian; network; political; user 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. doi = nan id = cord-198449-cru40qp4 author = Carballosa, Alejandro title = Incorporating social opinion in the evolution of an epidemic spread date = 2020-07-09 keywords = model; network; opinion 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). doi = nan id = cord-283793-ab1msb2m author = Chanchan, Li title = Modeling and analysis of epidemic spreading on community network with node's birth and death date = 2016-10-31 keywords = epidemic; network summary = doi = 10.1016/s1005-8885(16)60061-4 id = cord-273941-gu6nnv9d author = Chandran, Uma title = Chapter 5 Network Pharmacology date = 2017-12-31 keywords = TCM; Triphala; chinese; disease; drug; network; target summary = doi = 10.1016/b978-0-12-801814-9.00005-2 id = cord-163462-s4kotii8 author = Chaoub, Abdelaali title = 6G for Bridging the Digital Divide: Wireless Connectivity to Remote Areas date = 2020-09-09 keywords = Fig; University; network; remote 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. doi = nan id = cord-133273-kvyzuayp author = Christ, Andreas title = Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020) date = 2020-10-05 keywords = CNN; Fig; ICU; base; datum; feature; figure; learn; model; network; result; robot; system 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. doi = nan id = cord-048461-397hp1yt author = Coelho, Flávio C title = Epigrass: a tool to study disease spread in complex networks date = 2008-02-26 keywords = Epigrass; model; network 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): doi = 10.1186/1751-0473-3-3 id = cord-134926-dk28wutc author = Dasgupta, Anirban title = Scalable Estimation of Epidemic Thresholds via Node Sampling date = 2020-07-28 keywords = Chung; network 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. doi = nan id = cord-295307-zrtixzgu author = Delgado-Chaves, Fernando M. title = Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks date = 2020-07-21 keywords = MHV; deg; gene; ly6e; network 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 . doi = 10.3390/genes11070831 id = cord-308249-es948mux author = Dokuka, Sofia title = How academic achievement spreads: The role of distinct social networks in academic performance diffusion date = 2020-07-27 keywords = academic; network; social summary = doi = 10.1371/journal.pone.0236737 id = cord-319055-r16dd0vj author = Dumitrescu, Cătălin title = Development of an Acoustic System for UAV Detection † date = 2020-08-28 keywords = MFCC; acoustic; detection; drone; network summary = doi = 10.3390/s20174870 id = cord-282035-jibmg4ch author = Dunbar, R. I. M. title = Structure and function in human and primate social networks: implications for diffusion, network stability and health date = 2020-08-26 keywords = Dunbar; individual; layer; network; size; social; structure summary = doi = 10.1098/rspa.2020.0446 id = cord-029277-mjpwkm2u author = Elboher, Yizhak Yisrael title = An Abstraction-Based Framework for Neural Network Verification date = 2020-06-13 keywords = dnn; network 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). doi = 10.1007/978-3-030-53288-8_3 id = cord-285872-rnayrws3 author = Elgendi, Mohamed title = The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias date = 2020-08-18 keywords = covid-19; network 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. doi = 10.3389/fmed.2020.00550 id = cord-285647-9tegcrc3 author = Estrada, Ernesto title = Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS-CoV-2 date = 2020-08-17 keywords = PPI; SARS; network; process; protein 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. doi = 10.1063/5.0015626 id = cord-324256-5tzup41p author = Feng, Shanshan title = Infectious diseases spreading on a metapopulation network coupled with its second-neighbor network date = 2019-11-15 keywords = SNN; network summary = doi = 10.1016/j.amc.2019.05.005 id = cord-016448-7imgztwe author = Frishman, D. title = Protein-protein interactions: analysis and prediction date = 2009-10-01 keywords = Cytoscape; Fig; PSI; datum; domain; interaction; network; protein summary = doi = 10.1007/978-3-211-75123-7_17 id = cord-028685-b1eju2z7 author = Fuentes, Ivett title = Rough Net Approach for Community Detection Analysis in Complex Networks date = 2020-06-10 keywords = community; network 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. doi = 10.1007/978-3-030-52705-1_30 id = cord-342579-kepbz245 author = Galaz, Victor title = Global networks and global change-induced tipping points date = 2014-05-01 keywords = CCAMLR; Galaz; global; iuu; network; point summary = doi = 10.1007/s10784-014-9253-6 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-290033-oaqqh21e author = Georgalakis, James title = A disconnected policy network: The UK''s response to the Sierra Leone Ebola epidemic date = 2020-02-13 keywords = Ebola; Leone; Sierra; network; policy 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.) . doi = 10.1016/j.socscimed.2020.112851 id = cord-218639-ewkche9r author = Ghavasieh, Arsham title = Multiscale statistical physics of the Human-SARS-CoV-2 interactome date = 2020-08-21 keywords = PPI; SARS; network 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. doi = nan id = cord-031663-i71w0es7 author = Giacobbe, Mirco title = How Many Bits Does it Take to Quantize Your Neural Network? date = 2020-03-13 keywords = SMT; bit; network; neural 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. doi = 10.1007/978-3-030-45237-7_5 id = cord-280648-1dpsggwx author = Gillen, David title = Regulation, competition and network evolution in aviation date = 2005-05-31 keywords = Airlines; Canada; FSA; network; vba 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. doi = 10.1016/j.jairtraman.2005.03.002 id = cord-336747-8m7n5r85 author = Grossmann, G. title = Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study date = 2020-05-08 keywords = model; network; ode 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. doi = 10.1101/2020.05.05.20091736 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-319658-u0wjgw50 author = Guven-Maiorov, Emine title = Structural host-microbiota interaction networks date = 2017-10-12 keywords = host; interaction; network; protein summary = doi = 10.1371/journal.pcbi.1005579 id = cord-241057-cq20z1jt author = Han, Jungmin title = Statistical Physics of Epidemic on Network Predictions for SARS-CoV-2 Parameters date = 2020-07-06 keywords = model; network 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] . doi = nan 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-206872-t6lr3g1m author = Huang, Huawei title = A Survey of State-of-the-Art on Blockchains: Theories, Modelings, and Tools date = 2020-07-07 keywords = Bitcoin; Blockchain; Ethereum; author; network; transaction summary = doi = nan id = cord-303197-hpbh4o77 author = Humboldt-Dachroeden, Sarah title = The state of one health research across disciplines and sectors – a bibliometric analysis date = 2020-06-06 keywords = Health; network 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). doi = 10.1016/j.onehlt.2020.100146 id = cord-340827-vx37vlkf author = Jackson, Matthew O. title = Chapter 14 Diffusion, Strategic Interaction, and Social Structure date = 2011-12-31 keywords = Jackson; Social; action; agent; chapter; model; network summary = doi = 10.1016/b978-0-444-53187-2.00014-0 id = cord-015861-lg547ha9 author = Kang, Nan title = The Realization Path of Network Security Technology Under Big Data and Cloud Computing date = 2019-03-12 keywords = cloud; network 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 doi = 10.1007/978-981-13-7123-3_66 id = cord-024571-vlklgd3x author = Kim, Yushim title = Community Analysis of a Crisis Response Network date = 2019-07-28 keywords = Korea; community; network; organization; response 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. doi = 10.1177/0894439319858679 id = cord-024346-shauvo3j author = Kruglov, Vasiliy N. title = Using Open Source Libraries in the Development of Control Systems Based on Machine Vision date = 2020-05-05 keywords = network; neural 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. doi = 10.1007/978-3-030-47240-5_7 id = cord-027286-mckqp89v author = Ksieniewicz, Paweł title = Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks date = 2020-05-23 keywords = model; network; optimization 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. doi = 10.1007/978-3-030-50423-6_16 id = cord-006292-rqo10s2g author = Kumar, Sameer title = Bonded-communities in HantaVirus research: a research collaboration network (RCN) analysis date = 2016-04-07 keywords = author; network; paper; research 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. doi = 10.1007/s11192-016-1942-1 id = cord-343419-vl6gkoin author = Lee, Pei-Chun title = Quantitative mapping of scientific research—The case of electrical conducting polymer nanocomposite date = 2010-07-10 keywords = China; University; actor; network summary = doi = 10.1016/j.techfore.2010.06.002 id = cord-127900-78x19fw4 author = Leung, Abby title = Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies date = 2020-10-06 keywords = CGEM; model; network 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 doi = nan 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-338588-rc1h4drd author = Li, Xuanyi title = Seven decades of chemotherapy clinical trials: a pan-cancer social network analysis date = 2020-10-16 keywords = author; gender; impact; network; score; trial 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. doi = 10.1038/s41598-020-73466-6 id = cord-306654-kal6ylkd author = Li, Yuhong title = Ripple Effect in the Supply Chain Network: Forward and Backward Disruption Propagation, Network Health and Firm Vulnerability date = 2020-10-10 keywords = disruption; network; propagation; supply summary = doi = 10.1016/j.ejor.2020.09.053 id = cord-024742-hc443akd author = Liu, Quan-Hui title = Epidemic spreading on time-varying multiplex networks date = 2018-12-03 keywords = layer; network 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. doi = 10.1103/physreve.98.062303 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-155440-7l8tatwq author = Malinovskaya, Anna title = Online network monitoring date = 2020-10-19 keywords = chart; control; network 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 doi = nan id = cord-253711-a0prku2k author = Mao, Liang title = Coupling infectious diseases, human preventive behavior, and networks – A conceptual framework for epidemic modeling date = 2011-11-26 keywords = behavior; disease; individual; network summary = doi = 10.1016/j.socscimed.2011.10.012 id = cord-259634-ays40jlz author = Marcelino, Jose title = Critical paths in a metapopulation model of H1N1: Efficiently delaying influenza spreading through flight cancellation date = 2012-05-15 keywords = Jaccard; edge; network 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. doi = 10.1371/4f8c9a2e1fca8 id = cord-125979-2c2agvex author = Mata, Ang''elica S. title = An overview of epidemic models with phase transitions to absorbing states running on top of complex networks date = 2020-10-05 keywords = SIS; network 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. doi = nan id = cord-003887-4grjr0h3 author = McClure, Ryan S. title = Unified feature association networks through integration of transcriptomic and proteomic data date = 2019-09-17 keywords = CLR; Fig; MINET; edge; genie3; network 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. doi = 10.1371/journal.pcbi.1007241 id = cord-024830-cql4t0r5 author = McMillin, Stephen Edward title = Quality Improvement Innovation in a Maternal and Child Health Network: Negotiating Course Corrections in Mid-Implementation date = 2020-05-08 keywords = implementation; network; program; screening 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. doi = 10.1007/s42972-020-00004-z id = cord-018054-w863h0d3 author = Mirchev, Miroslav title = Non-poisson Processes of Email Virus Propagation date = 2010 keywords = email; network 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. doi = 10.1007/978-3-642-10781-8_20 id = cord-103150-e9q8e62v author = Mishra, Shreya title = Improving gene-network inference with graph-wavelets and making insights about ageing associated regulatory changes in lungs date = 2020-11-04 keywords = at2; cell; expression; gene; network 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 ). doi = 10.1101/2020.07.24.219196 id = cord-200354-t20v00tk author = Miya, Taichi title = Experimental Analysis of Communication Relaying Delay in Low-Energy Ad-hoc Networks date = 2020-10-29 keywords = OLSR; delay; network 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. doi = nan id = cord-220116-6i7kg4mj author = Mukhamadiarov, Ruslan I. title = Social distancing and epidemic resurgence in agent-based Susceptible-Infectious-Recovered models date = 2020-06-03 keywords = SIR; figure; network 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. doi = nan id = cord-102776-2upbx2lp author = Niu, Zhibin title = Visual analytics for networked-guarantee loans risk management date = 2017-04-06 keywords = default; guarantee; network; risk summary = doi = 10.1109/pacificvis.2018.00028 id = cord-034833-ynti5g8j author = Nosonovsky, Michael title = Scaling in Colloidal and Biological Networks date = 2020-06-04 keywords = ANN; Shannon; figure; layer; network; surface 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. doi = 10.3390/e22060622 id = cord-164703-lwwd8q3c author = Noury, Zahra title = Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment date = 2020-06-15 keywords = captcha; image; network 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. doi = nan id = cord-103418-deogedac author = Ochab, J. K. title = Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks date = 2010-11-12 keywords = epidemic; network 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. doi = 10.1140/epjb/e2011-10975-6 id = cord-266771-zesp6q0w author = Pablo-Martí, Federico title = Complex networks to understand the past: the case of roads in Bourbon Spain date = 2020-10-06 keywords = España; Fig; Madrazo; Madrid; Spain; century; map; network; road; spanish; transport summary = doi = 10.1007/s11698-020-00218-x id = cord-019055-k5wcibdk author = Pacheco, Jorge M. title = Disease Spreading in Time-Evolving Networked Communities date = 2017-10-05 keywords = disease; individual; network 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. doi = 10.1007/978-981-10-5287-3_13 id = cord-148358-q30zlgwy author = Pang, Raymond Ka-Kay title = An analysis of network filtering methods to sovereign bond yields during COVID-19 date = 2020-09-28 keywords = MST; european; network 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. doi = nan id = cord-225177-f7i0sbwt author = Pastor-Escuredo, David title = Characterizing information leaders in Twitter during COVID-19 crisis date = 2020-05-14 keywords = Twitter; network 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 ). doi = nan id = cord-011400-zyjd9rmp author = Peixoto, Tiago P. title = Network Reconstruction and Community Detection from Dynamics date = 2019-09-18 keywords = model; network; reconstruction 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 . doi = 10.1103/physrevlett.123.128301 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-338127-et09wi82 author = Qin, Bosheng title = Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19 date = 2020-09-14 keywords = facemask; image; network summary = doi = 10.3390/s20185236 id = cord-327651-yzwsqlb2 author = Ray, Bisakha title = Network inference from multimodal data: A review of approaches from infectious disease transmission date = 2016-09-06 keywords = bayesian; datum; method; network; transmission 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. doi = 10.1016/j.jbi.2016.09.004 id = cord-346309-hveuq2x9 author = Reis, Ben Y title = An Epidemiological Network Model for Disease Outbreak Detection date = 2007-06-26 keywords = datum; model; network; stream summary = doi = 10.1371/journal.pmed.0040210 id = cord-333088-ygdau2px author = Roy, Manojit title = On representing network heterogeneities in the incidence rate of simple epidemic models date = 2006-03-31 keywords = Fig; model; network 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. doi = 10.1016/j.ecocom.2005.09.001 id = cord-143847-vtwn5mmd author = Ryffel, Th''eo title = ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing date = 2020-06-08 keywords = function; model; network; protocol 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. doi = nan id = cord-033557-fhenhjvm author = Saha, Debdatta title = Reconciling conflicting themes of traditionality and innovation: an application of research networks using author affiliation date = 2020-10-09 keywords = Amla; Ashwagandha; Ayurveda; author; network 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. doi = 10.1007/s13596-020-00515-w id = cord-027719-98tjnry7 author = Said, Abd Mlak title = Machine Learning Based Rank Attack Detection for Smart Hospital Infrastructure date = 2020-05-31 keywords = IDS; network; rpl 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. doi = 10.1007/978-3-030-51517-1_3 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-234918-puunbcio author = Shalu, Hrithwik title = A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays date = 2020-08-19 keywords = COVID-19; Network; image 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 doi = nan 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 = doi = 10.1007/978-3-030-45439-5_54 id = cord-346606-bsvlr3fk author = Siriwardhana, Yushan title = The role of 5G for digital healthcare against COVID-19 pandemic: Opportunities and challenges date = 2020-11-04 keywords = COVID-19; healthcare; network; service 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. doi = 10.1016/j.icte.2020.10.002 id = cord-230294-bjy2ixcj author = Stella, Massimo title = #lockdown: network-enhanced emotional profiling at the times of COVID-19 date = 2020-05-09 keywords = English; emotional; hashtag; italian; network summary = doi = nan id = cord-002929-oqe3gjcs author = Strano, Emanuele title = Mapping road network communities for guiding disease surveillance and control strategies date = 2018-03-16 keywords = Fig; community; network; road 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) . doi = 10.1038/s41598-018-22969-4 id = cord-015967-kqfyasmu author = Tagore, Somnath title = Epidemic Models: Their Spread, Analysis and Invasions in Scale-Free Networks date = 2015-03-20 keywords = epidemic; individual; infection; network 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] . doi = 10.1007/978-3-319-15916-4_1 id = cord-328858-6xqyllsl author = Tajeddini, Kayhan title = Enhancing hospitality business performance: The role of entrepreneurial orientation and networking ties in a dynamic environment date = 2020-07-15 keywords = Tajeddini; business; entrepreneurial; firm; network 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. doi = 10.1016/j.ijhm.2020.102605 id = cord-010758-ggoyd531 author = Valdano, Eugenio title = Epidemic Threshold in Continuous-Time Evolving Networks date = 2018-02-06 keywords = network; time 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: doi = 10.1103/physrevlett.120.068302 id = cord-307735-6pf7fkvq author = Walkey, Allan J. title = The Viral Infection and Respiratory Illness Universal Study (VIRUS): An International Registry of Coronavirus 2019-Related Critical Illness date = 2020-04-29 keywords = Discovery; Network summary = doi = 10.1097/cce.0000000000000113 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-027851-95bsoea2 author = Wang, Daojuan title = Coupling between financing and innovation in a startup: embedded in networks with investors and researchers date = 2020-06-25 keywords = coupling; entrepreneur; innovation; network 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. doi = 10.1007/s11365-020-00681-y id = cord-003297-fewy8y4a author = Wang, Ming-Yang title = A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs date = 2018-09-18 keywords = PPI; QSTR; aconitine; figure; network; protein 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 ). doi = 10.3390/molecules23092385 id = cord-017423-cxua1o5t author = Wang, Rui title = A Review of Microblogging Marketing Based on the Complex Network Theory date = 2011-11-12 keywords = model; network 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. doi = 10.1007/978-1-4419-8849-2_134 id = cord-288024-1mw0k5yu author = Wang, Wei title = Entrepreneurial entry: The role of social media date = 2020-09-29 keywords = entrepreneurial; individual; medium; network; social 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. doi = 10.1016/j.techfore.2020.120337 id = cord-354783-2iqjjema author = Wang, Wei title = Containing misinformation spreading in temporal social networks date = 2019-04-24 keywords = network summary = doi = 10.1063/1.5114853 id = cord-104001-5clslvqb author = Wang, Xiaoqi title = selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks date = 2020-10-21 keywords = network; path; prediction; representation 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. doi = 10.1101/2020.10.20.347153 id = cord-352049-68op3d8t author = Wang, Xingyuan title = Model of epidemic control based on quarantine and message delivery date = 2016-09-15 keywords = Fig; disease; network summary = doi = 10.1016/j.physa.2016.04.009 id = cord-262100-z6uv32a0 author = Wang, Yuanyuan title = Changes in network centrality of psychopathology symptoms between the COVID-19 outbreak and after peak date = 2020-09-14 keywords = COVID-19; network; symptom 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. doi = 10.1038/s41380-020-00881-6 id = cord-288342-i37v602u author = Wang, Zhen title = Coupled disease–behavior dynamics on complex networks: A review date = 2015-07-08 keywords = Fig; behavior; disease; epidemic; individual; model; network summary = doi = 10.1016/j.plrev.2015.07.006 id = cord-191876-03a757gf author = Weinert, Andrew title = Processing of Crowdsourced Observations of Aircraft in a High Performance Computing Environment date = 2020-08-03 keywords = Network; aircraft; model 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. doi = nan id = cord-256713-tlluxd11 author = Welch, David title = Is Network Clustering Detectable in Transmission Trees? date = 2011-06-03 keywords = clustering; network 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. doi = 10.3390/v3060659 id = cord-312817-gskbu0oh author = Witte, Carmel title = Spatiotemporal network structure among “friends of friends” reveals contagious disease process date = 2020-08-06 keywords = bird; friend; network 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. doi = 10.1371/journal.pone.0237168 id = cord-332313-9m2iozj3 author = Yang, Hyeonchae title = Structural efficiency to manipulate public research institution networks date = 2016-01-13 keywords = change; institution; network; public; research; structural summary = doi = 10.1016/j.techfore.2015.12.012 id = cord-256707-kllv27bl author = Zhang, Jun title = Evolution of Chinese airport network date = 2010-09-15 keywords = Fig; network 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. doi = 10.1016/j.physa.2010.05.042 id = cord-024552-hgowgq41 author = Zhang, Ruixi title = Hydrological Process Surrogate Modelling and Simulation with Neural Networks date = 2020-04-17 keywords = DEM; model; network 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. doi = 10.1007/978-3-030-47436-2_34 id = cord-322815-r82iphem author = Zhang, Weiping title = Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network date = 2020-07-04 keywords = block; network; risk; sector summary = doi = 10.1016/j.najef.2020.101248 id = cord-317435-4yuw7jo3 author = Zhou, Yadi title = Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 date = 2020-03-16 keywords = Fig; MERS; SARS; drug; network summary = doi = 10.1038/s41421-020-0153-3 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 id = cord-314498-zwq67aph author = van Heck, Eric title = Smart business networks: Concepts and empirical evidence date = 2009-05-15 keywords = business; network 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. doi = 10.1016/j.dss.2009.05.002 id = cord-007708-hr4smx24 author = van Kampen, Antoine H. C. title = Taking Bioinformatics to Systems Medicine date = 2015-08-13 keywords = datum; disease; expression; gene; network; system 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. doi = 10.1007/978-1-4939-3283-2_2 id = cord-318716-a525bu7w author = van den Oord, Steven title = Network of networks: preliminary lessons from the Antwerp Port Authority on crisis management and network governance to deal with the COVID‐19 pandemic date = 2020-06-02 keywords = APA; Antwerp; Port; Provan; network summary = doi = 10.1111/puar.13256