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Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects
Martina Morris1, Mark S Handcock, David R Hunter
1Departments of Sociology and Statistics University of Washington Seattle, WA 98195, United States of America.
Journal of Statistical Software
|July 25, 2008
Summary
Exponential-family random graph models (ERGMs) use network statistics to model link formation. This guide details available statistics and Markov chain Monte Carlo (MCMC) estimation controls within the ergm package for network analysis.
Area of Science:
- Network Science
- Statistical Modeling
- Computational Social Science
Background:
- Exponential-family random graph models (ERGMs) are statistical tools for understanding network structures.
- ERGMs represent link formation processes using user-selected network statistics.
- These statistics define the probability distribution over possible network configurations.
Purpose of the Study:
- To describe available statistics for ERGMs in the ergm package.
- To explain methods for controlling the Markov chain Monte Carlo (MCMC) algorithm used for ERGM estimation.
- To detail other essential arguments for core ergm package functions.
Main Methods:
- Description of various classes of network statistics available in the ergm package.
- Explanation of Markov chain Monte Carlo (MCMC) algorithm controls for ERGM estimation.
- Overview of parameters influencing the Metropolis-Hastings algorithm and sample space constraints.
Main Results:
- Comprehensive catalog of network statistics for ERGM implementation.
- Detailed guidance on optimizing MCMC estimation through algorithm and constraint control.
- User-friendly documentation of ergm package functionalities.
Conclusions:
- The ergm package provides a robust framework for applying ERGMs.
- Effective control over MCMC estimation enhances the reliability of network model results.
- Understanding available statistics and controls is crucial for accurate network analysis.
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