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Infinite multiple relational models for complex networks

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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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