Properties of Latent Variable Network Models

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Show simple item record Rastelli, Riccardo Friel, Nial Raftery, Adrian E. 2017-03-10T17:23:29Z 2017-03-10T17:23:29Z 2016 Cambridge University Press en 2016-12-12
dc.identifier.citation Network Science en
dc.description.abstract We derive properties of Latent Variable Models for networks, a broad class ofmodels that includes the widely-used Latent Position Models. These include theaverage degree distribution, clustering coefficient, average path length and degreecorrelations. We introduce the Gaussian Latent Position Model, and derive analyticexpressions and asymptotic approximations for its network properties. Wepay particular attention to one special case, the Gaussian Latent Position Modelswith Random Effects, and show that it can represent the heavy-tailed degree distributions,positive asymptotic clustering coefficients and small-world behaviours thatare often observed in social networks. Several real and simulated examples illustratethe ability of the models to capture important features of observed networks. en
dc.description.sponsorship Science Foundation Ireland en
dc.language.iso en en
dc.publisher Cambridge University Press en
dc.subject Machine learning en
dc.subject Statistics en
dc.subject Fitness models en
dc.subject Latent position models en
dc.subject Latent variable models en
dc.subject Random graphs en
dc.subject Social networks en
dc.title Properties of Latent Variable Network Models en
dc.type Journal Article en
dc.status Peer reviewed en
dc.identifier.volume 4 en
dc.identifier.issue 4 en
dc.identifier.startpage 407 en
dc.identifier.endpage 432 en
dc.identifier.doi 10.1017/nws.2016.23
dc.neeo.contributor Rastelli|Riccardo|aut|
dc.neeo.contributor Friel|Nial|aut|
dc.neeo.contributor Raftery|Adrian E.|aut|
dc.description.othersponsorship Eunice Kennedy Shriver National Institute of Child Health and Development en
dc.description.othersponsorship National Institutes of Health en 2016-12-05T12:06:02Z

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