Conference paper
Infinite Multiple Membership Relational Modeling for Complex Networks
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2010 |
Pages: | 1-6 |
Proceedings: | NIPS Workshop 2010 |
ISBN: | 1457716216 , 1457716224 , 9781457716218 , 9781457716225 , 1457716232 and 9781457716232 |
ISSN: | 21610363 and 15512541 |
Types: | Conference paper |
DOI: | 10.1109/MLSP.2011.6064546 |
ORCIDs: | Mørup, Morten , Schmidt, Mikkel Nørgaard and Hansen, Lars Kai |
Analytical models Bayes methods Communities Complex networks Computational modeling Data models Proposals Stochastic processes complex networks large scale networks latent structure learning learning (artificial intelligence) link prediction multiple membership analysis networked data nonparametric Bayesian multiple membership latent feature model nonparametric statistics single membership relational model