Properties of Latent Variable Network Models
|Title:||Properties of Latent Variable Network Models||Authors:||Rastelli, Riccardo
Raftery, Adrian E.
|Permanent link:||http://hdl.handle.net/10197/8393||Date:||12-Dec-2016||Online since:||2017-03-10T17:23:29Z||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Cambridge University Press||Journal:||Network Science||Volume:||4||Issue:||4||Start page:||407||End page:||432||Copyright (published version):||2016 Cambridge University Press||Keywords:||Machine learning; Statistics; Fitness models; Latent position models; Latent variable models; Random graphs; Social networks||DOI:||10.1017/nws.2016.23||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
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