Variational Bayesian inference for the Latent Position Cluster Model

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dc.contributor.author Salter-Townshend, Michael
dc.contributor.author Murphy, Thomas Brendan
dc.date.accessioned 2011-02-15T12:03:40Z
dc.date.available 2011-02-15T12:03:40Z
dc.date.copyright 2009 NIPS Foundation en
dc.date.issued 2009-12
dc.identifier.uri http://hdl.handle.net/10197/2756
dc.description Analyzing Networks and Learning with Graphs Workshop at 23rd annual conference on Neural Information Processing Systems (NIPS 2009), Whister, December 11 2009 en
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland en
dc.description.uri Conference details en
dc.description.uri http://nips.cc/Conferences/2009/ en
dc.description.uri Workshop website en
dc.description.uri http://snap.stanford.edu/nipsgraphs2009/ en
dc.format.extent 418814 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.requires Mathematical Sciences Research Collection en
dc.rights All Rights reserved en
dc.subject Networks en
dc.subject Bayes en
dc.subject Variational en
dc.subject.lcsh Social networks--Mathematical models en
dc.subject.lcsh Cluster analysis en
dc.subject.lcsh Bayesian statistical decision theory en
dc.title Variational Bayesian inference for the Latent Position Cluster Model en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.internal.webversions Workshop website version en
dc.internal.webversions http://snap.stanford.edu/nipsgraphs2009/papers/townshend-paper.pdf en
dc.status Peer reviewed en
dc.neeo.contributor Salter-Townshend|Michael|aut| en
dc.neeo.contributor Murphy|Thomas Brendan|aut| en
dc.description.admin Conference site: http://nips.cc/Conferences/2009/ - AV 28/01/2011 en


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