Learning Latent Block Structure in Weighted Networks
Abstract
Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar communitylevel connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network's largescale structure, which can facilitate its scientific interpretation and the prediction of unknown or future interactions. Popular approaches, including the stochastic block model, assume edges are unweighted, which limits their utility by throwing away potentially useful information. We introduce the `weighted stochastic block model' (WSBM), which generalizes the stochastic block model to networks with edge weights drawn from any exponential family distribution. This model learns from both the presence and weight of edges, allowing it to discover structure that would otherwise be hidden when weights are discarded or thresholded. We describe a Bayesian variational algorithm for efficiently approximating this model's posterior distribution over latent block structures. We then evaluate the WSBM's performance on both edgeexistence and edgeweight prediction tasks for a set of realworld weighted networks. In all cases, the WSBM performs as well or better than the best alternatives on these tasks.
 Publication:

arXiv eprints
 Pub Date:
 April 2014
 DOI:
 10.48550/arXiv.1404.0431
 arXiv:
 arXiv:1404.0431
 Bibcode:
 2014arXiv1404.0431A
 Keywords:

 Statistics  Machine Learning;
 Computer Science  Social and Information Networks;
 Physics  Data Analysis;
 Statistics and Probability;
 Physics  Physics and Society
 EPrint:
 28 Pages