Properties of Latent Variable Network Models

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Title: Properties of Latent Variable Network Models
Authors: Rastelli, Riccardo
Friel, Nial
Raftery, Adrian E.
Permanent link: http://hdl.handle.net/10197/8393
Date: 12-Dec-2016
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
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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