Bayesian Hierarchical Community Discovery

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• Yee Whye Teh (University of Oxford; The Alan Turing Institute)
• Tuesday 26 July 2016, 14:00-14:30
• Seminar Room 1, Newton Institute.

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SNAW05 - Bayesian methods for networks

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.

Related Links

• http://papers.nips.cc/paper/5048-bayesian-hierarchical-community-discovery – NIPS paper page 

This talk is part of the Isaac Newton Institute Seminar Series series.

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