Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

Abstract

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them them to partition networks of collaboration in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.

Post-peer-review version, 23 pages, 7 figures, 2 tables, combined article and supplementary information

References in corpus (5)

• Community detection in networks with positive and negative links
• Analysis of community structure in networks of correlated data
• Community Structure in Congressional Cosponsorship Networks
• backbone: An R Package for extracting the backbone of bipartite projections
• Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs

Cited by in corpus (4)

• backbone: An R package to extract network backbones
• Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections
• Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity
• Negative Ties Highlight Hidden Extremes in Social Media Polarization