Date of Original Version

4-2010

Type

Article

Abstract or Description

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.

DOI

10.1214/09-EJS548

Creative Commons


This work is licensed under a Creative Commons Attribution 3.0 License.

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Published In

Electronic Journal of Statistics, 4, 585-605.