Bayesian model selection for the latent position cluster model for Social Networks

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Title: Bayesian model selection for the latent position cluster model for Social Networks
Authors: Friel, Nial
Ryan, Catriona
Wyse, Jason
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Date: Mar-2017
Online since: 2017-06-09T09:02:12Z
Abstract: 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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Cambridge University Press
Volume: 5
Issue: 1
Start page: 70
End page: 91
Copyright (published version): 2017 Cambridge University Press
Keywords: Machine learningStatisticsCollapsed latent position cluster modelReversible jump Markov chain Monte CarloBayesian model choiceSocial network analysisFinite mixture model
DOI: 10.1017/nws.2017.6
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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