There abound temporal (i.e., time-varying) network data, which have been stimulating modeling, theory, development of algorithms, data analytics, and applications of temporal networks. First, I discuss explanation of heavy-tailed distributions of inter-contact times by ``state-dynamics models'' in which each node is assumed to flip among a small number of discrete states and the nodes' states determine time-dependent edges. This approach is interpretable, facilitates mathematical analyses, and seeds various research (e.g., theorizing on epidemic thresholds, understanding of metapopulation models, inference of mixtures of exponential distributions, new Gillespie algorithms, dimension reduction of temporal network data), some of which we will also discuss. Second, I briefly discuss modeling of temporal networks by ``switching networks'', which allows analytical understanding of dynamics on temporal networks. Finally, I will discuss some of my interdisciplinary collaborations including data analysis methods we have been developing for these applications.
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