An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks
This addresses the challenge of understanding polarization and group dynamics in social systems like online discourse and political divisions, though it is incremental as it builds on prior local search methods by extending them to include neutral vertices.
The paper tackled the problem of detecting polarized communities in signed networks, which often suffer from size-imbalanced solutions, by proposing a novel optimization objective and the first local search algorithm that handles neutral vertices and scales to large networks, resulting in consistent outperformance of state-of-the-art baselines in solution quality while maintaining competitive computational efficiency.
Signed networks, where edges are labeled as positive or negative to represent friendly or antagonistic interactions, provide a natural framework for analyzing polarization, trust, and conflict in social systems. Detecting meaningful group structures in such networks is crucial for understanding online discourse, political divisions, and trust dynamics. A key challenge is to identify communities that are internally cohesive and externally antagonistic, while allowing for neutral or unaligned vertices. In this paper, we propose a method for identifying $k$ polarized communities that addresses a major limitation of prior methods: their tendency to produce highly size-imbalanced solutions. We introduce a novel optimization objective that avoids such imbalance. In addition, it is well known that approximation algorithms based on local search are highly effective for clustering signed networks when neutral vertices are not allowed. We build on this idea and design the first local search algorithm that extends to the setting with neutral vertices while scaling to large networks. By connecting our approach to block-coordinate Frank-Wolfe optimization, we prove a linear convergence rate, enabled by the structure of our objective. Experiments on real-world and synthetic datasets demonstrate that our method consistently outperforms state-of-the-art baselines in solution quality, while remaining competitive in computational efficiency.