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Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks


Bruce A. Desmarais is an Assistant Professor in the Department of Political Science and the Computational Social Science Initiative at the University of Massachusetts Amherst. His current research focuses on modeling interdependent political processes with specialties in network analysis and collective decision making. He has published articles in Political Analysis, Public Choice, State Politics and Policy Quarterly, Social Networks, Physica A, PLoS ONE, International Interactions, Conflict Management and Peace Science, and the Proceedings of the IEEE.

Skyler J. Cranmer is an Assistant Professor in the Department of Political Science at the University of North Carolina at Chapel Hill. His current research lies at the intersection of political methodology and international relations with an emphasis on network analysis for the study of international conflict. He has published articles in the Journal of Politics, British Journal of Political Science, Political Analysis, International Interactions, Conflict Management and Peace Science, Social Networks, Policy Studies Journal, Physica A, PLoS ONE, Twin Research and Human Genetics, and the Proceedings of the IEEE.


The exponential random graph model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used to flexibly analyze the processes by which policy actors organize into a network. Often times, interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed triangles in a network). This limits the utility of the ERGM because there is often interest, particularly in political and policy sciences, in network dynamics at the actor or relationship levels. Micro-level interpretation of the ERGM has been employed in varied applications in sociology and statistics. We present a comprehensive framework for interpretation of the ERGM at all levels of analysis, which casts network formation as block-wise updating of a network. These blocks can represent, for example, each potential link, each dyad, the out- or in-going ties of each actor, or the entire network. We contrast this interpretive framework with the stochastic actor-based model (SABM) of network dynamics. We present the theoretical differences between the ERGM and the SABM and introduce an approach to comparing the models when theory is not sufficiently strong to make the selection a priori. The alternative models we discuss and the interpretation methods we propose are illustrated on previously published data on estuary policy and governance networks.