Infinite multiple membership relational modeling for complex networks

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2011

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Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiplemembership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show on “real” size benchmark network data that accounting for multiple memberships improves the learning of latent structure as measured by link prediction while explicitly accounting for multiple membership result in a more compact representation of the latent structure of networks.
Original languageEnglish
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Publication date2011
ISBN (print)978-1-4577-1621-8
ISBN (electronic)978-1-4577-1622-5
StatePublished - 2011


Conference2011 IEEE International Workshop on Machine Learning for Signal Processing
Period01/01/2011 → …
Internet address
CitationsWeb of Science® Times Cited: No match on DOI
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ID: 5886522