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August 2013 Pseudo-likelihood methods for community detection in large sparse networks
Arash A. Amini, Aiyou Chen, Peter J. Bickel, Elizaveta Levina
Ann. Statist. 41(4): 2097-2122 (August 2013). DOI: 10.1214/13-AOS1138

Abstract

Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks. Here we propose a new fast pseudo-likelihood method for fitting the stochastic block model for networks, as well as a variant that allows for an arbitrary degree distribution by conditioning on degrees. We show that the algorithms perform well under a range of settings, including on very sparse networks, and illustrate on the example of a network of political blogs. We also propose spectral clustering with perturbations, a method of independent interest, which works well on sparse networks where regular spectral clustering fails, and use it to provide an initial value for pseudo-likelihood. We prove that pseudo-likelihood provides consistent estimates of the communities under a mild condition on the starting value, for the case of a block model with two communities.

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Arash A. Amini. Aiyou Chen. Peter J. Bickel. Elizaveta Levina. "Pseudo-likelihood methods for community detection in large sparse networks." Ann. Statist. 41 (4) 2097 - 2122, August 2013. https://doi.org/10.1214/13-AOS1138

Information

Published: August 2013
First available in Project Euclid: 23 October 2013

zbMATH: 1277.62166
MathSciNet: MR3127859
Digital Object Identifier: 10.1214/13-AOS1138

Subjects:
Primary: 62G20
Secondary: 62H99

Keywords: Community detection , network , pseudo-likelihood

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.41 • No. 4 • August 2013
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