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Bayesian Hierarchical Community Discovery

Presented by: 
Yee Whye Teh
Tuesday 26th July 2016 - 14:00 to 14:30
INI Seminar Room 1
Co-author: Charles Blundell (Google DeepMind)

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.

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons