Electronic Journal of Statistics

Discrete temporal models of social networks

Steve Hanneke, Wenjie Fu, and Eric P. Xing

Full-text: Open access

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.

Article information

Source
Electron. J. Statist. Volume 4 (2010), 585-605.

Dates
First available: 16 June 2010

Permanent link to this document
http://projecteuclid.org/euclid.ejs/1276694116

Digital Object Identifier
doi:10.1214/09-EJS548

Mathematical Reviews number (MathSciNet)
MR2660534

Zentralblatt MATH identifier
06166518

Citation

Hanneke, Steve; Fu, Wenjie; Xing, Eric P. Discrete temporal models of social networks. Electronic Journal of Statistics 4 (2010), 585--605. doi:10.1214/09-EJS548. http://projecteuclid.org/euclid.ejs/1276694116.


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