Probabilistic graph partitioning

author: David Barber, Centre for Computational Statistics and Machine Learning, University College London
published: Sept. 7, 2007,   recorded: September 2007,   views: 6748

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We consider the problem of Graph Partitioning for applications in Web Mining and Collaborative Filtering. Our approach is based on predicting the presence/absence of a directed link based on a form of probabilistic mixture model. Being based on a generative model of directed graphs, we are able to apply an approximate Bayesian treatment to automatically select an appropriate number of partitions. We will discuss an application in Collaborative Filtering and comment on relations to mixed membership models, Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis.

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