Statistical Inference for Multilayer Networks in Political Science

35 Pages Posted: 19 Jun 2018

See all articles by Ted Hsuan Yun Chen

Ted Hsuan Yun Chen

Department of Political Science, Pennsylvania State University

Date Written: May 4, 2018

Abstract

Interactions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model (ERGM). In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.

Keywords: multilayer networks, network analysis, policy communication, global conflict

Suggested Citation

Chen, Ted Hsuan Yun, Statistical Inference for Multilayer Networks in Political Science (May 4, 2018). Available at SSRN: https://ssrn.com/abstract=3189835 or http://dx.doi.org/10.2139/ssrn.3189835

Ted Hsuan Yun Chen (Contact Author)

Department of Political Science, Pennsylvania State University ( email )

203 Pond Lab
University Park, PA 16802
United States

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