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Inferential Network Analysis with Exponential Random Graph Models

Published online by Cambridge University Press:  04 January 2017

Skyler J. Cranmer  and
Bruce A. Desmarais
Skyler J. Cranmer*
Affiliation:
Department of Political Science, University of North Carolina at Chapel Hill, 311 Hamilton Hall, Chapel Hill, NC 27599
Bruce A. Desmarais
Affiliation:
Department of Political Science, University of Massachusetts Amherst, Thompson Hall, 200 Hicks Way, Amherst, MA 01003. e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
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Abstract

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Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network data remain fledgling by comparison. We introduce and evaluate a general model for inference with network data, the Exponential Random Graph Model (ERGM) and several of its recent extensions. The ERGM simultaneously allows both inference on covariates and for arbitrarily complex network structures to be modeled. Our contributions are three-fold: beyond introducing the ERGM and discussing its limitations, we discuss extensions to the model that allow for the analysis of non-binary and longitudinally observed networks and show through applications that network-based inference can improve our understanding of political phenomena.

Type
Research Article
Information
Political Analysis , Volume 19 , Issue 1 , Winter 2011 , pp. 66 - 86
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: Many thanks to Tom Carsey, James Fowler, Justin Gross, and Peter Mucha for their valuable comments on a previous version of this article. Supplementary materials for this article are available on the Political Analysis Web site.

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