Infinite Multiple Membership Relational Modeling for Complex Networks
|Morten Mørup, Mikkel N. Schmidt, Lars K. Hansen|
|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|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|