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Bayesian model selection for the latent position cluster model for social networks

Published online by Cambridge University Press:  03 April 2017

CAITRÍONA RYAN ,
JASON WYSE  and
NIAL FRIEL
CAITRÍONA RYAN
Affiliation:
Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland (e-mail: Caitriona.Ryan@ul.ie)
JASON WYSE
Affiliation:
Discipline of Statistics, School of Computer Science and Statistics, Trinity College Dublin, College Green, Dublin 2, Ireland (e-mail: wyseja@tcd.ie)
NIAL FRIEL
Affiliation:
School of Mathematics and Statistics and Insight: The National Centre for Big Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland (e-mail: nial.friel@ucd.ie)
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Abstract

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The latent position cluster model is a popular model for the statistical analysis of network data. This model assumes that there is an underlying latent space in which the actors follow a finite mixture distribution. Moreover, actors which are close in this latent space are more likely to be tied by an edge. This is an appealing approach since it allows the model to cluster actors which consequently provides the practitioner with useful qualitative information. However, exploring the uncertainty in the number of underlying latent components in the mixture distribution is a complex task. The current state-of-the-art is to use an approximate form of BIC for this purpose, where an approximation of the log-likelihood is used instead of the true log-likelihood which is unavailable. The main contribution of this paper is to show that through the use of conjugate prior distributions, it is possible to analytically integrate out almost all of the model parameters, leaving a posterior distribution which depends on the allocation vector of the mixture model. This enables posterior inference over the number of components in the latent mixture distribution without using trans-dimensional MCMC algorithms such as reversible jump MCMC. Our approach is compared with the state-of-the-art latentnet (Krivitsky & Handcock, 2015) and VBLPCM (Salter-Townshend & Murphy, 2013) packages.

Keywords

collapsed latent position cluster modelreversible jump Markov chain Monte CarloBayesian model choicesocial network analysisfinite mixture model

Information

Type
Research Article
Information
Network Science , Volume 5 , Issue 1 , March 2017 , pp. 70 - 91
Copyright
Copyright © Cambridge University Press 2017 

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