Variational Bayesian inference for the Latent Position Cluster Model

DSpace/Manakin Repository

Show simple item record Salter-Townshend, Michael Murphy, Thomas Brendan 2011-02-15T12:03:40Z 2011-02-15T12:03:40Z 2009 NIPS Foundation en 2009-12
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 en
dc.description.uri Workshop website en
dc.description.uri 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 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: - AV 28/01/2011 en

This item appears in the following Collection(s)

Show simple item record

This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

If you are a publisher or author and have copyright concerns for any item, please email and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.

Search Research Repository

Advanced Search