Epidemics on a Stochastic Model of Temporal Network

Rocha, Luis E. C.; Decuyper, Adeline; Blondel, Vincent D.

doi:10.1007/978-1-4614-6729-8_15

Cited by 13 publications

(10 citation statements)

References 24 publications

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“…In contrast, at small per-contact infection probabilities, where the final size of the infected population is presumably small, temporal networks yield a larger final size than the randomized networks do. Different numerical simulations with the SI model support similar conclusions [81]. Miritello et al [37] conclude that group conversation contacts (i.e., correlations between the contact sequences of adjacent links–see Fig.…”

Section: Epidemic Spreading On Temporal Networkmentioning

confidence: 53%

Predicting and controlling infectious disease epidemics using temporal networks

Masuda¹,

Holme²

2013

F1000Prime Rep

185

176

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Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

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Section: Epidemic Spreading On Temporal Networkmentioning

confidence: 53%

Predicting and controlling infectious disease epidemics using temporal networks

Masuda¹,

Holme²

2013

F1000Prime Rep

185

176

View full text Add to dashboard Cite

show abstract

“…In this section, we describe the procedure for generating the temporal networks on a regular random graph. A similar algorithm for generating temporal networks based on IEIs on nodes, not links, was proposed recently [46]. Firstly, we generate a regular random graph with N = 163…”

Section: Appendix a Strength Of Weak Ties Property In The Conferencementioning

confidence: 99%

Importance of individual events in temporal networks

et al. 2012

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Records of time-stamped social interactions between pairs of individuals (e.g. face-to-face conversations, e-mail exchanges and phone calls) constitute a so-called temporal network. A remarkable difference between temporal networks and conventional static networks is that time-stamped events rather than links are the unit elements generating the collective behavior of nodes. We propose an importance measure for single interaction events. By generalizing the concept of the advance of events proposed by Kossinets et al (2008 Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining p 435), we propose that an event is central when it carries new information about others to the two nodes involved in the event. We find that the proposed measure properly quantifies the importance of events in connecting nodes along time-ordered paths.

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“…Therefore, new metrics specifically designed to characterize the temporal properties of graph sequences have been proposed, and most of the classical metrics defined for static graphs have been extended to the time-varying case [44][45][46][47][48][49][50]. Recently, the study of dynamical processes taking place on time-evolving graphs has shown that temporal correlations and contact recurrence play a fundamental role in diverse settings such as random walks dynamics [51][52][53], the spreading of information and diseases [54][55][56], and synchronization [57].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary dynamics of time-resolved social interactions

et al. 2014

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Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.

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Epidemics on a Stochastic Model of Temporal Network

Cited by 13 publications

References 24 publications

Predicting and controlling infectious disease epidemics using temporal networks

Predicting and controlling infectious disease epidemics using temporal networks

Importance of individual events in temporal networks

Evolutionary dynamics of time-resolved social interactions

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