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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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
