Fast online graph clustering via Erdös-Rényi mixture

In the context of graph clustering, we consider the problem of estimating simultaneously both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size with time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erdös Régnyi Graph Mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data is a network extracted from the french political blogosphere and presents an interesting community organization.