Quantifying Global Networks of Exchange through the Louvain Method

TL;DR

This paper constructs and analyzes a global inter-country network based on shared interests in CRS reports, using the Louvain method to identify communities and influential countries.

Contribution

It introduces a novel network analysis of CRS report data, applying community detection and centrality measures to reveal global connectivity patterns.

Findings

Identified distinct country communities with shared policy interests.

Highlighted influential countries based on eigenvector centrality.

Provided insights into global policy network structure.

Abstract

Congressional Research Service (CRS) reports provide detailed analyses of major policy issues to members of the US Congress. We extract and analyze data from 2,010 CRS reports written between 1996 and 2024 to quantify inter-country relationships, representing 172 countries as nodes and 4,137 shared interests as edges within a weighted, bidirectional network. Through the Louvain method, we extract non-overlapping communities from our network and identify clusters with shared interests. We then compute the eigenvector centrality of countries to highlight their network influence. The results of this work could enable improvements in sourcing evidence for analytic products and understanding the connectivity of our world.