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 multiplemembership latent feature model for networks. Contrary to existing multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-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 multiplememberships 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.
|Publication status||Published - 2010|
|Event||NIPS Workshop on Networks Across Disciplines in Theory and Application - Whistler, Canada|
Duration: 1 Jan 2010 → …
|Conference||NIPS Workshop on Networks Across Disciplines in Theory and Application|
|Period||01/01/2010 → …|