Bayesian nonparametric models for bipartite graphs
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 behav...
| 1. autor: | |
|---|---|
| Format: | Journal article |
| Język: | English |
| Wydane: |
2012
|
| Streszczenie: | 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, a generative process for network growth, and a simple Gibbs sampler for posterior simulation. Our model is shown to be well fitted to several real-world social networks. |
|---|