A Mixture of Experts Latent Position Cluster Model for Social Network Data

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Title: A Mixture of Experts Latent Position Cluster Model for Social Network Data
Authors: Gormley, Isobel Claire
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/7116
Date: May-2010
Abstract: Social network data represent the interactions between a group of social actors. Interactions between colleagues and friendship networks are typical examples of such data. The latent space model for social network data locates each actor in a network in a latent (social) space and models the probability of an interaction between two actors as a function of their locations. The latent position cluster model extends the latent space model to deal with network data in which clusters of actors exist — actor locations are drawn from a finite mixture model, each component of which represents a cluster of actors. A mixture of experts model builds on the structure of a mixture model by taking account of both observations and associated covariates when modeling a heterogeneous population. Herein, a mixture of experts extension of the latent position cluster model is developed. The mixture of experts framework allows covariates to enter the latent position cluster model in a number of ways, yielding different model interpretations. Estimates of the model parameters are derived in a Bayesian framework using a Markov Chain Monte Carlo algorithm. The algorithm is generally computationally expensive — surrogate proposal distributions which shadow the target distributions are derived, reducing the computational burden. The methodology is demonstrated through an illustrative example detailing relationships between a group of lawyers in the USA.
Type of material: Journal Article
Publisher: Elsevier
Copyright (published version): 2010 Elsevier
Keywords: ClusteringCovariatesLatent spaceMixture modelsMixture of experts modelsSocial network dataSurrogate proposal distributions
DOI: 10.1016/j.stamet.2010.01.002
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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