Scalable Inference of Overlapping Communities

author: Prem Gopalan, Department of Computer Science, Princeton University
published: Jan. 11, 2013,   recorded: December 2012,   views: 2627
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Description

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.

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Download slides icon Download slides: machine_gopalan_overlapping_01.pdf (371.0 KB)

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