Analyzing and visualizing polarization and balance with signed networks: the U.S. Congress case study

TL;DR

This paper introduces a spectral analysis pipeline for signed networks to study polarization and balance, applied to U.S. Congress data from 1945 to 2020, offering efficient and detailed insights into political dynamics.

Contribution

It presents a novel, computationally efficient spectral method for analyzing signed networks, enabling detailed visualization and assessment of polarization in political networks.

Findings

Identified key congressmen influencing network balance.

Tracked polarization evolution in U.S. Congress from 1945 to 2020.

Demonstrated the method's ability to analyze subgraph contributions.

Abstract

Signed networks and balance theory provide a natural setting for real-world scenarios that show polarization dynamics, positive/negative relationships, and political partisanship. For example, they have been proven effective in studying the increasing polarization of the votes in the two chambers of the U.S. Congress from World War II on. To provide further insights into this particular case study, we propose the application of a pipeline to analyze and visualize a signed graph's configuration based on the exploitation of the corresponding Laplacian matrix' spectral properties. The overall methodology is comparable with others based on the frustration index, but it has at least two main advantages: first, it requires a much lower computational cost; second, it allows for a quantitative and visual assessment of how arbitrarily small subgraphs (even single nodes) contribute to the overall balance (or unbalance) of the network. The proposed pipeline allows the exploration of polarization dynamics shown by the U.S. Congress from 1945 to 2020 at different resolution scales. In fact, we are able to spot and point out the influence of some (groups of) congressmen in the overall balance, as well as to observe and explore polarization's evolution of both chambers across the years.