2025/09/02 by Barucca, Paolo · 1 citation
#05C82 #60G55 #60K35 #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #G.2.2 #G.3 #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2509.02098
Temporal networks consist of timestamped directed interactions that may appear continuously in time, yet few studies have directly tackled the continuous-time modeling of networks. Here, we introduce a maximum-entropy approach to temporal networks and with basic assumptions on constraints, the corresponding network ensembles admit a modular and interpretable representation: a set of global time processes and a static maximum-entropy edge, e.g. node pair, probability. This time-edge labels factorization yields closed-form log-likelihoods, degree, clustering and motif expectations, and yields a whole class of effective generative models. We provide maximum-entropy derivation of an inhomogeneous Poisson edge intensity for temporal networks via functional optimization over path entropy, connecting NHPP modeling to maximum-entropy network ensembles. NHPP consistently improve log-likelihood over generic Poisson processes, while the maximum-entropy edge labels recover strength constraints and reproduce expected unique-degree curves. We discuss the limitations of this framework and how it can be integrated with multivariate Hawkes calibration procedures, renewal theory, and neural kernel estimation in graph neural networks.