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dc.contributor.authorVu, Duy Q.
Hunter, David R.
Schweinberger, Michael
dc.date.accessioned 2017-05-03T18:24:05Z
dc.date.available 2017-05-03T18:24:05Z
dc.date.issued 2013
dc.identifier.citation Vu, Duy Q., Hunter, David R. and Schweinberger, Michael. "Model-based clustering of large networks." The Annals of Applied Statistics, 7, no. 2 (2013) Project Euclid: 1010-1039. https://doi.org/10.1214/12-AOAS617.
dc.identifier.urihttps://hdl.handle.net/1911/94136
dc.description.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.
dc.language.iso eng
dc.publisher Project Euclid
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.title Model-based clustering of large networks
dc.type Journal article
dc.citation.journalTitle The Annals of Applied Statistics
dc.subject.keywordsocial networks
stochastic block models
finite mixture models
EM algorithms
generalized EM algorithms
variational EM algorithms
MM algorithms
dc.citation.volumeNumber 7
dc.citation.issueNumber 2
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1214/12-AOAS617
dc.identifier.pmcid PMC4655199
dc.identifier.pmid 26605002
dc.type.publication publisher version
dc.citation.firstpage 1010
dc.citation.lastpage 1039


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