Bayesian nonparametric models for bipartite graphs

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

Bibtex Metadata Paper Supplemental


Francois Caron


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.