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Bayesian nonparametric models for bipartite graphs

Bayesian nonparametric models for bipartite graphs

This video was recorded at 26th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe 2012. 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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