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Scalable Inference of Overlapping Communities

Author(s): Gopalan, Prem; Mimno, David; Gerrish, Sean M; Freedman, Michael J; Blei, David M

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Abstract: We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel. It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60,000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of communities in large real-world networks, and detects the true communities in 280 benchmark networks with equal or better accuracy compared to other scalable algorithms.
Publication Date: 2012
Citation: Gopalan, Prem K., Sean Gerrish, Michael Freedman, David M. Blei, and David M. Mimno. "Scalable Inference of Overlapping Communities." In Advances in Neural Information Processing Systems 25, pp. 2249-2257. 2012.
ISSN: 1049-5258
Pages: 2249 - 2257
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems 25
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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