Bayesian nonparametric models for bipartite graphs

author: François Caron, INRIA Bordeaux - Sud-Ouest
published: Jan. 16, 2013,   recorded: December 2012,   views: 219
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Description

We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on the theory of completely random measures and is able to handle a potentially infinite number of nodes. We show that the model has appealing properties and in particular it may exhibit a power-law behavior. We derive a posterior characterization, an Indian Buffet-like generative process for network growth, and a simple and efficient Gibbs sampler for posterior simulation. Our model is shown to be well fitted to several real-world social networks.

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