NIPS Proceedingsβ

Bayesian Hierarchical Community Discovery

Part of: Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Conference Event Type: Poster


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.