Community Structure in the United Nations General Assembly

TL;DR

This paper analyzes the community structure of UN General Assembly voting networks over several decades using different network models and community detection methods to understand voting groupings.

Contribution

It compares three network representations of voting data and evaluates their effectiveness in identifying voting communities, highlighting the importance of multiple resolution parameters.

Findings

Different network models reveal varying community structures.

Multiple resolution parameters improve community detection accuracy.

Signed networks effectively capture agreement and disagreement in votes.

Abstract

We study the community structure of networks representing voting on resolutions in the United Nations General Assembly. We construct networks from the voting records of the separate annual sessions between 1946 and 2008 in three different ways: (1) by considering voting similarities as weighted unipartite networks; (2) by considering voting similarities as weighted, signed unipartite networks; and (3) by examining signed bipartite networks in which countries are connected to resolutions. For each formulation, we detect communities by optimizing network modularity using an appropriate null model. We compare and contrast the results that we obtain for these three different network representations. In so doing, we illustrate the need to consider multiple resolution parameters and explore the effectiveness of each network representation for identifying voting groups amidst the large amount of agreement typical in General Assembly votes.