Naoki Masuda, Department of Mathematics at SUNY Buffalo
Will present a talk entitled:
State-dynamics view of temporal networks and heavy tails
Abstract: Two salient features of empirical temporal (i.e., time-varying) network data are the time-varying nature of network structure and heavy-tailed distributions of inter-contact times. Both of these features have a large impact on dynamical processes occurring on networks and populations, such as contagion processes. In this presentation, we introduce state-dynamics modeling approaches in which each node or the entire network, depending on the assumption, switches among a small number of discrete states (e.g., a human individual flips between a high-activity and low-activity states) over time in a Markovian manner. This assumption facilitates both (i) intuitive understanding of the dynamics of nodes and networks and (ii) theoretical analyses. Furthermore, this approach provides models that mimic heavy-tailed distributions of interevent times (with a mathematical reason) and other features present in empirical data of temporal contact networks. With this modeling framework, we discuss inter-contact times, metapopulation models, epidemic spreading, and estimation of system-state dynamics from temporal network data.
SC166
Friday April 8, 2022
12:00 pm
