Maximum-entropy temporal networks
NetSI Speaker Series
Harrison Hartle
Network Science PhD Candidate
Past Talk
Hybrid talk
Wednesday
Aug 3, 2022
Watch video
11:00 am
177 Huntington Ave
Virtual
11th floor
Online
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This talk will be hybrid in-person and remote.

An important question for temporal network modeling is that of null model selection. We address this problem by applying the principle of maximum entropy to temporal networks. This allows the derivation of probability distributions on graph-sequences that are as unbiased as possible while satisfying a chosen set of constraints, be they exact or expectation-based. We investigate the many options for constraint-choices, and show how such choices impact the resulting dynamic networks. Constraints can be devised such that the model simultaneously yields (a) tunable network properties within each snapshot, and (b) tunable dynamic properties relating to graph-structure across multiple snapshots. Depending on the form of the latter constraint-types, the resulting temporal networks can be sequences of independently sampled graphs, first-order Markov chains, higher-order Markov chains, or altogether non-Markovian. We construct such models in both discrete and continuous time, with time-homogeneous and time-inhomogeneous constraints, studying them analytically and in large-scale simulations. We show that only certain combinations of constraints are allowed, and explore the parameter spaces of valid constraint-value combinations. We derive maximum-entropy temporal versions of well-known static network models, study their properties, compare them to various well-known temporal network models, and apply this framework to data from several real-world temporal networks. Additionally, we highlight interesting questions and remaining challenges.

About the speaker
About the speaker

Harrison is a fifth-year PhD candidate working with Prof. Dmitri Krioukov in DK-lab on theoretical aspects of a variety of statistical ensembles of random networks.  He graduated with an undergraduate degree in Physics in 2017 from the University of Alaska, Fairbanks, and has a research background in partial differential equations, two-dimensional fluid turbulence, and spatiotemporally chaotic oscillator systems.