It is now widely accepted that knowledge can be learnt from networks by clustering their vertices according to connection profiles. Many deterministic and probabilistic methods have been developed. Given a network, almost all them partition the vertices into disjoint clusters. However, recent studies have shown that these methods were too restrictive and that most of the existing networks contained overlapping clusters. To tackle this issue, we propose the Overlapping Stochastic Block Model (OSBM). Our approach allows the vertices to belong to multiple clusters, and, to some extent, generalizes the well known Stochastic Block Model (SBM). We show that the model is generically identifiable within classes of equivalence and we propose an approximate inference procedure, based 1 in ria -0 04 94 82 0, v er si on 1 24 J un 2 01 0 Manuscrit auteur, publié dans "42èmes Journées de Statistique (2010)"