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.