Bayesian Hierarchical Community Discovery
Abstract
We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks. Our model is a tree-structured mixture of potentially exponentially many stochastic blockmodels. We describe a family of greedy agglomerative model selection algorithms whose worst case scales quadratically in the number of vertices of the network, but independent of the number of communities. Our algorithms are two orders of magnitude faster than the infinite relational model, achieving comparable or better accuracy.
Cite
Text
Blundell and Teh. "Bayesian Hierarchical Community Discovery." Neural Information Processing Systems, 2013.Markdown
[Blundell and Teh. "Bayesian Hierarchical Community Discovery." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/blundell2013neurips-bayesian/)BibTeX
@inproceedings{blundell2013neurips-bayesian,
title = {{Bayesian Hierarchical Community Discovery}},
author = {Blundell, Charles and Teh, Yee Whye},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {1601-1609},
url = {https://mlanthology.org/neurips/2013/blundell2013neurips-bayesian/}
}