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Scalable Inference of Overlapping Communities
Prem Gopalan · David Mimno · Sean Gerrish · Michael Freedman · David Blei

Wed Dec 05 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

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.

Author Information

Prem Gopalan (The Voleon Group)
David Mimno (Cornell University)
Sean Gerrish (Princeton University)
Michael Freedman (Princeton University)
David Blei (Columbia University)

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