Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL

TL;DR

This paper introduces a novel block modeling approach for dynamic networks using non-homogeneous Poisson processes, with an exact likelihood criterion for simultaneous clustering and model selection, validated through experiments.

Contribution

It develops an exact integrated classification likelihood criterion for dynamic network block modeling with non-homogeneous Poisson processes, enabling simultaneous cluster estimation and model regularization.

Findings

Effective clustering of dynamic network nodes demonstrated.

Regularized model reduces over-fitting in intensity estimation.

Method performs well on real and simulated data.

Abstract

We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes. In a block modelling perspective, nodes belong to hidden clusters (whose number is unknown) and the intensity functions of the counting processes only depend on the clusters of nodes. In order to make inference tractable we move to discrete time by partitioning the entire time horizon in which interactions are observed in fixed-length time sub-intervals. First, we derive an exact integrated classification likelihood criterion and maximize it relying on a greedy search approach. This allows to estimate the memberships to clusters and the number of clusters simultaneously. Then a maximum-likelihood estimator is developed to estimate non parametrically the integrated intensities. We discuss the over-fitting problems of the model and propose a regularized version solving these issues. Experiments on real and simulated data are carried out in order to assess the proposed methodology.