Infinite Multiple Membership Relational Modeling for Complex Networks

Morten Mørup, Mikkel N. Schmidt, Lars K. Hansen

AbstractLearning 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 multiple-membership 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.
TypeConference paper [With referee]
ConferencePresented first time at NIPS Workshop on Networks Across Disciplines in Theory and Application, published IEEE MLSP 2011
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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