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.
| Statistical Inference for Multilayer Networks in Political Science was authored by Ted Hsuan Yun Chen. It was published by Cambridge in PSR&M in 2021. |
