A Mixture of Experts Latent Position Cluster Model for Social Network Data
|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:||Clustering; Covariates; Latent space; Mixture models; Mixture of experts models; Social network data; Surrogate 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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