Bayesian Nonparametric Models for Bipartite Graphs
Abstract
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.
Cite
Text
Caron. "Bayesian Nonparametric Models for Bipartite Graphs." Neural Information Processing Systems, 2012.Markdown
[Caron. "Bayesian Nonparametric Models for Bipartite Graphs." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/caron2012neurips-bayesian/)BibTeX
@inproceedings{caron2012neurips-bayesian,
title = {{Bayesian Nonparametric Models for Bipartite Graphs}},
author = {Caron, Francois},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {2051-2059},
url = {https://mlanthology.org/neurips/2012/caron2012neurips-bayesian/}
}