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 member-ship result in a more compact representation of the latent structure of networks.
- Morten Mørup, Mikkel N. Schmidt, and Lars Kai Hansen, Infinite multiple membership relational modeling for complex networks, Machine Learning for Signal Processing, IEEE International Workshop on (MLSP), 2011
title = "Infinite multiple membership relational modeling for complex networks",
author = "Morten Mørup and Mikkel N. Schmidt and Lars Kai Hansen ",
booktitle = "Machine Learning for Signal Processing, IEEE International Workshop on (MLSP)",
year = "2011"