Model Selection in Overlapping Stochastic Block Models

Abstract: Networks are a commonly used mathematical model to de-scribe the rich set of interactions between objects of interest. Many clus-tering methods have been developed in order to partition such structures, among which several rely on underlying probabilistic models, typically mix-ture models. The relevant hidden structure may however show overlapping groups in several applications. The Overlapping Stochastic Block Model [Latouche, Birmele ́ and Ambroise (2011)] has been developed to take this phenomenon into account. Nevertheless, the problem of the choice of the number of classes in the inference step is still open. To tackle this issue, we consider the proposed model in a Bayesian framework and develop a new criterion based on a non asymptotic approximation of the marginal log-likelihood. We describe how the criterion can be computed through a variational Bayes EM algorithm, and demonstrate its efficiency by running it on both simulated and real data