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2016 Empirical Bayes estimation for the stochastic blockmodel
Shakira Suwan, Dominic S. Lee, Runze Tang, Daniel L. Sussman, Minh Tang, Carey E. Priebe
Electron. J. Statist. 10(1): 761-782 (2016). DOI: 10.1214/16-EJS1115


Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks (connectomics), etc. Recent theoretical developments have shown that spectral embedding of graphs yields tractable distributional results; in particular, a random dot product latent position graph formulation of the stochastic blockmodel informs a mixture of normal distributions for the adjacency spectral embedding. We employ this new theory to provide an empirical Bayes methodology for estimation of block memberships of vertices in a random graph drawn from the stochastic blockmodel, and demonstrate its practical utility. The posterior inference is conducted using a Metropolis-within-Gibbs algorithm. The theory and methods are illustrated through Monte Carlo simulation studies, both within the stochastic blockmodel and beyond, and experimental results on a Wikipedia graph are presented.


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Shakira Suwan. Dominic S. Lee. Runze Tang. Daniel L. Sussman. Minh Tang. Carey E. Priebe. "Empirical Bayes estimation for the stochastic blockmodel." Electron. J. Statist. 10 (1) 761 - 782, 2016.


Received: 1 April 2015; Published: 2016
First available in Project Euclid: 22 March 2016

zbMATH: 1333.62088
MathSciNet: MR3477741
Digital Object Identifier: 10.1214/16-EJS1115

Primary: 62F15
Secondary: 62H30

Keywords: Adjacency spectral graph embedding , Bayesian inference , random dot product graph model , stochastic blockmodel

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society


Vol.10 • No. 1 • 2016
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