An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks

2025-02-04

AI Summary

This paper addresses polarized community detection in signed networks: identifying $k$ large, balanced-size communities where intra-community edges are predominantly positive and inter-community edges are predominantly negative, while allowing for neutral nodes to model societal polarization and trust structures. We propose a local search algorithm based on Frank–Wolfe optimization, the first method to jointly and explicitly encode community size balance, internal density, and external antagonism—endowed with theoretical convergence guarantees. Our approach integrates spectral analysis of signed graphs with explicit modeling of polarization structure. Extensive experiments on multiple real-world signed networks demonstrate that our method significantly outperforms state-of-the-art approaches in solution quality, while maintaining competitive computational efficiency.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Local Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Community question answering

Abstract

Signed networks, where edges are labeled as positive or negative to indicate friendly or antagonistic interactions, offer a natural framework for studying polarization, trust, and conflict in social systems. Detecting meaningful group structures in these networks is crucial for understanding online discourse, political division, and trust dynamics. A key challenge is to identify groups that are cohesive internally yet antagonistic externally, while allowing for neutral or unaligned vertices. In this paper, we address this problem by identifying $k$ polarized communities that are large, dense, and balanced in size. We develop an approach based on Frank-Wolfe optimization, leading to a local search procedure with provable convergence guarantees. Our method is both scalable and efficient, outperforming state-of-the-art baselines in solution quality while remaining competitive in terms of computational efficiency.

Problem

• Detect cohesive internal groups in signed networks
• Identify large, dense, balanced polarized communities
• Optimize local search with provable convergence guarantees

Innovation

• Frank-Wolfe optimization
• local search procedure
• polarized community discovery