Infinite multiple relational models for complex networks

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• Mikkel N. Schmidt (Technical University of Denmark / Cambridge)
• Thursday 27 January 2011, 14:00-15:30
• Engineering Department, CBL Room 438.

If you have a question about this talk, please contact Shakir Mohamed.

Learning latent structure in complex networks is an important problem that has many application areas. In this talk I present a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-membership models that scale quadratically in the number of vertices, the proposed model scales linearly in the number of links. I present an efficient split-merge inference procedure that significantly outperform standard Gibbs sampling.

This talk is part of the Machine Learning Reading Group @ CUED series.

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