Show simple item record Brew, Anthony Salter-Townshend, Michael 2011-04-08T12:16:08Z 2011-04-08T12:16:08Z 2010-12-11
dc.description NIPS Workshop on Networks across Disciplines in Theory and Applications, 11th December 2010, Whistler BC, Canada en
dc.description.abstract Network modeling can be approached using either discriminative or probabilistic models. In the task of link prediction a probabilistic model will give a probability for the existence of a link; while in some scenarios this may be beneficial, in others a hard discriminative boundary needs to be set. Hence the use of a discriminative classifier is preferable. In domains such as image analysis and speaker recognition, probabilistic models have been used as a mechanism from which features can be extracted. This paper examines using a probabilistic model built on the entire graph to extract features to predict the existence of unknown links between two nodes. It demonstrates how features extracted from the model as well as the predicted probability of a link existing can aid the classification process. en
dc.description.sponsorship Science Foundation Ireland en
dc.description.uri Conference website en
dc.description.uri en
dc.format.extent 241738 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.requires Mathematical Sciences Research Collection en
dc.subject Networks en
dc.subject Link prediction en
dc.subject Social network analysis en
dc.subject.lcsh Social sciences--Network analysis en
dc.subject.lcsh Social networks--Mathematical models en
dc.subject.lcsh Probabilities en
dc.subject.lcsh Cluster analysis en
dc.title A latent space mapping for link prediction en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.internal.webversions en
dc.status Peer reviewed en
dc.neeo.contributor Brew|Anthony|aut| en
dc.neeo.contributor Salter-Townshend|Michael|aut| en

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