Discrete temporal models of social networks
Steve Hanneke, Wenjie Fu, and Eric P. Xing
Source: Electron. J. Statist. Volume 4
(2010), 585-605.
Abstract
We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.
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Permanent link to this document: http://projecteuclid.org/euclid.ejs/1276694116
Digital Object Identifier: doi:10.1214/09-EJS548
Mathematical Reviews number (MathSciNet): MR2660534
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