Statistical inference for multilayer networks in political science

Ted Hsuan Yun Chen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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. 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.

Original languageEnglish
Pages (from-to)380-397
Number of pages18
JournalPolitical Science Research and Methods
Volume9
Issue number2
DOIs
Publication statusPublished - Apr 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Civil/domestic conflict
  • environmental politics and policy
  • international conflict
  • quantitative methods

Fingerprint

Dive into the research topics of 'Statistical inference for multilayer networks in political science'. Together they form a unique fingerprint.

Cite this