Learning Latent Block Structure in Weighted Networks

By Christopher Aicher, Abigail Z. Jacobs, Aaron Clauset. J. Complex Networks, 2014

336 citations recorded by the source corpus.

Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network's large-scale 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 edge-existence and edge-weight prediction tasks for a set of real-world weighted networks. In all cases, the WSBM performs as well or better than the best alternatives on these tasks.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6abb292510ec6dfdb7a845d316c2d61539670de8

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Citations in the corpus

Link Prediction Based on Graph Neural Networks (2018, methodology): “For SBM, we use the implementation of [43] using a latent group number 12.”
Community detection in networks: A user guide (2016, background): “, directed networks [63], networks with weighted edges [200], with overlapping communities [201], with multiple layers [118], with annotations [111,117].”
Structure and inference in annotated networks (2015, methodology): “Typically, rather than maximizing (2) itself, we maximize instead its logarithm,”
Weisfeiler-Lehman Neural Machine for Link Prediction (2017, methodology): “Popular latent feature-based methods include: matrix factorization [23]; ranking methods [24] which treat link prediction as a learning to rank problem; and stochastic block models [5, 25], which assume that nodes have latent groups and links are determined by the group memberships of nodes.”