The Annals of Applied Statistics

Model-based clustering of large networks

Duy Q. Vu, David R. Hunter, and Michael Schweinberger

Full-text: Open access

Abstract

We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data sets than those seen elsewhere in the literature. The more flexible framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms are based on variational generalized EM algorithms, where the E-steps are augmented by a minorization-maximization (MM) idea. The bootstrapped standard error estimates are based on an efficient Monte Carlo network simulation idea. Last, we demonstrate the usefulness of the model-based clustering framework by applying it to a discrete-valued network with more than 131,000 nodes and 17 billion edge variables.

Article information

Source
Ann. Appl. Stat., Volume 7, Number 2 (2013), 1010-1039.

Dates
First available in Project Euclid: 27 June 2013

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1372338477

Digital Object Identifier
doi:10.1214/12-AOAS617

Mathematical Reviews number (MathSciNet)
MR3113499

Zentralblatt MATH identifier
1288.62106

Keywords
Social networks stochastic block models finite mixture models EM algorithms generalized EM algorithms variational EM algorithms MM algorithms

Citation

Vu, Duy Q.; Hunter, David R.; Schweinberger, Michael. Model-based clustering of large networks. Ann. Appl. Stat. 7 (2013), no. 2, 1010--1039. doi:10.1214/12-AOAS617. https://projecteuclid.org/euclid.aoas/1372338477


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