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Abstract: In the context of graph clustering, we consider the problem of simultaneously estimating 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 over 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 Erdős–Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization. [Copyright &y& Elsevier]

Abstract: Large datasets with interactions between objects are common to numerous scientific fields including the social sciences and biology, as well as being a feature of specific phenomena such as the internet. The interactions naturally define a graph, and a common way of exploring and summarizing such datasets is graph clustering. Most techniques for clustering graph vertices use only the topology of connections, while ignoring information about the vertices’ features. In this paper we provide a clustering algorithm that harnesses both types of data, based on a statistical model with a latent structure characterizing each vertex both by a vector of features and by its connectivity. We perform simulations to compare our algorithm with existing approaches, and also evaluate our method using real datasets based on hypertext documents. We find that our algorithm successfully exploits whatever information is found both in the connectivity pattern and in the features. [Copyright &y& Elsevier]