Journal
ANNALS OF STATISTICS
Volume 41, Issue 4, Pages 2097-2122Publisher
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/13-AOS1138
Keywords
Community detection; network; pseudo-likelihood
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Funding
- NSF Focused Research Group [DMS-11-59005]
- NSF [DMS-11-06772]
- Direct For Mathematical & Physical Scien [1159005] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1106772] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1159005] Funding Source: National Science Foundation
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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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