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Modeling Overlapping Communities with Node Popularities

Author(s): Gopalan, Prem K; Wang, Chong; Blei, David

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Abstract: We develop a probabilistic approach for accurate network modeling using node popularities within the framework of the mixed-membership stochastic blockmodel (MMSB). Our model integrates two basic properties of nodes in social networks: homophily and preferential connection to popular nodes. We develop a scalable algorithm for posterior inference, based on a novel nonconjugate variant of stochastic variational inference. We evaluate the link prediction accuracy of our algorithm on nine real-world networks with up to 60,000 nodes, and on simulated networks with degree distributions that follow a power law. We demonstrate that the AMP predicts significantly better than the MMSB.
Publication Date: 2013
Citation: Gopalan, Prem, Chong Wang, and David M. Blei. "Modeling Overlapping Communities with Node Popularities." In Advances in Neural Information Processing Systems 26 (2013): pp. 2850-2858.
ISSN: 1049-5258
Pages: 2850 - 2858
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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