Infinite Multiple Membership Relational Modeling for Complex Networks |
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| Abstract | 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 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. |
| Type | Conference paper [With referee] |
| Conference | Presented first time at NIPS Workshop on Networks Across Disciplines in Theory and Application, published IEEE MLSP 2011 |
| Year | 2010 |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |