Variational Bayesian inference for the Latent Position Cluster Model

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Title: Variational Bayesian inference for the Latent Position Cluster Model
Authors: Salter-Townshend, Michael
Murphy, Thomas Brendan
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Date: Dec-2009
Online since: 2011-02-15T12:03:40Z
Abstract: Many recent approaches to modeling social networks have focussed on embedding the actors in a latent “social space”. Links are more likely for actors that are close in social space than for actors that are distant in social space. In particular, the Latent Position Cluster Model (LPCM) [1] allows for explicit modelling of the clustering that is exhibited in many network datasets. However, inference for the LPCM model via MCMC is cumbersome and scaling of this model to large or even medium size networks with many interacting nodes is a challenge. Variational Bayesian methods offer one solution to this problem. An approximate, closed form posterior is formed, with unknown variational parameters. These parameters are tuned to minimize the Kullback-Leibler divergence between the approximate variational posterior and the true posterior, which known only up to proportionality. The variational Bayesian approach is shown to give a computationally efficient way of fitting the LPCM. The approach is demonstrated on a number of data sets and it is shown to give a good fit.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Copyright (published version): 2009 NIPS Foundation
Keywords: NetworksBayesVariational
Subject LCSH: Social networks--Mathematical models
Cluster analysis
Bayesian statistical decision theory
Other versions: Workshop website version
Language: en
Status of Item: Peer reviewed
Conference Details: Analyzing Networks and Learning with Graphs Workshop at 23rd annual conference on Neural Information Processing Systems (NIPS 2009), Whister, December 11 2009
Appears in Collections:Computer Science Research Collection
Mathematics and Statistics Research Collection

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