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Properties of Latent Variable Network Models
Date Issued
2016-12-12
Date Available
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.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Eunice Kennedy Shriver National Institute of Child Health and Development
National Institutes of Health
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
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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31
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