title: MODEL-BASED CLUSTERING OF LARGE NETWORKS.
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.
authors.affiliation: Department of Mathematics and Statistics University of Melbourne Parkville, Victoria 3010 Australia duy.vu@unimelb.edu.au.
authors.forename: Duy Q
authors.initials: DQ
authors.lastname: Vu
authors.affiliation: Department of Statistics Pennsylvania State University University Park, Pennsylvania 16802 USA dhunter@stat.psu.edu.
authors.forename: David R
authors.initials: DR
authors.lastname: Hunter
authors.affiliation: Department of Statistics Rice University MS 138 P.O. Box 1892 Houston, Texas 77251-1892 USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Schweinberger
keywords: EM algorithms; MM algorithms; Social networks; finite mixture models; generalized EM algorithms; stochastic block models; variational EM algorithms