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In network science, identifying optimal partitions of a signed network intointernally cohesive and mutually divisive clusters based on generalized balancetheory is computationally challenging. We reformulate and generalize two binarylinear programming models that tackle this challenge, demonstrating theirpracticality by applying them them to partition networks of collaboration inthe US House of Representatives. These models guarantee a globally optimalnetwork partition and can be practically applied to signed networks containingup to 30,000 edges. In the US House context, we find that a three-clusterpartition is better than a conventional two-cluster partition, where theotherwise hidden third coalition is composed of highly effective legislatorswho are ideologically aligned with the majority party.

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