Embedding-aware Polarization Management in Signed Networks

2026-02-25

AI Summary

This work addresses a critical limitation in existing signed network embedding methods, which may inadvertently exacerbate structural polarization and lack explicit mechanisms to measure or mitigate polarization in the embedding space. To this end, we propose the Embedding-level Polarization Mitigation (EPM) framework, which uniquely unifies polarization handling at the embedding level. EPM introduces a novel polarization metric grounded in effective resistance theory, eliminating the need for predefined opinion states, and leverages structural balance theory to identify broker nodes for targeted local graph augmentation that alleviates polarization. Extensive experiments on real-world signed networks demonstrate that EPM effectively reduces polarization while preserving essential structural information required for downstream tasks.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Signed network embeddings (SNE) are widely used to represent networks with positive and negative relations, but their repeated use in downstream analysis pipelines can inadvertently reinforce structural polarization. Existing polarization measures are largely designed for unsigned networks or rely on predefined opinion states, limiting their applicability to embedding-based analysis in signed settings. We propose EPM, a unified polarization management framework that jointly measures and mitigates polarization in the embedding space. EPM introduces an embedding-based polarization measure grounded in effective resistance and a structure-aware mitigation strategy via localized augmentation through structurally balanced intermediary nodes. Experiments on real-world signed networks demonstrate that EPM effectively mitigates polarization while preserving task-relevant network structure. The codebase of EPM is available at https://github.com/JeonghanSon/EPM-Embedding-aware-Polarization-Management.

Problem

• signed networks
• network embedding
• polarization
• structural polarization
• embedding-based analysis

Innovation

• signed network embedding
• polarization measurement
• effective resistance
• structural balance
• embedding-aware mitigation