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(will be reviewed).

Beyond Siloed Analysis: How Multilayer Models Solve Political Network Inference Problems

Political interactions often span multiple overlapping relational contexts, an interdependence traditional network models struggle to capture. This paper introduces a multilayer approach using exponential random graph modeling (ERGM) that specifically addresses this challenge by integrating multiple relations.

Data & Methods 🎯

  • Examines two political networks: policy communication and global conflict data
  • Applies the new multilayer network methodology to these distinct systems

Key Findings 🔍

  • Models incorporating interdependence between relational contexts fit observed data significantly better than traditional single-layer approaches.
  • This multilayer method provides crucial inferential leverage for understanding complex political systems with interconnected relationships.

The approach demonstrates how accounting for multi-relational complexity enhances our analytical capabilities in political science.

Political Science Research & Methods
Statistical Inference for Multilayer Networks in Political Science was authored by Ted Hsuan Yun Chen. It was published in PSR&M in 2021. Find on Google Scholar10.1017/psrm.2019.49
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