Statistical Inference for Multilayer Networks in Political Science

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Authors Ted Hsuan Yun Chen
Paper Category
Paper 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. ∗Ted Hsuan Yun Chen is a postdoctoral researcher at Aalto University and University of Helsinki ( I would like to thank Bruce Desmarais, Mitch Goist, Boyoon Lee, Douglas Lemke, Fridolin Linder, Kevin Reuning, Xu Xu, participants at the 2018 PolNet Conference and the 2018 PolMeth Meeting, and Daniel Stegmueller and three anonymous reviewers at PSRM for providing valuable feedback at various points in this project. This research is part of the ECANET-consortium (Echo Chambers, Experts and Activists: Networks of Mediated Political Communication), part of the Media and Society research programme (2019-2022), funded by Academy of Finland (SA grant: 32779). Replication materials can be found at the PSRM Dataverse: Multilayer Networks in Political Science Chen ii
Date of publication 2018
Code Programming Language R

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