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A hierarchical gamma process infinite edge partition model is proposed tofactorize the binary adjacency matrix of an unweighted undirected relationalnetwork under a Bernoulli-Poisson link. The model describes both homophily andstochastic equivalence, and is scalable to big sparse networks by focusing itscomputation on pairs of linked nodes. It can not only discover overlappingcommunities and inter-community interactions, but also predict missing edges. Asimplified version omitting inter-community interactions is also provided andwe reveal its interesting connections to existing models. The number ofcommunities is automatically inferred in a nonparametric Bayesian manner, andefficient inference via Gibbs sampling is derived using novel data augmentationtechniques. Experimental results on four real networks demonstrate the models'scalability and state-of-the-art performance.

Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction