Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning

TL;DR

This paper introduces ReCon, a post-processing framework that improves community detection accuracy on signed networks by iteratively refining communities through structural, boundary, and contrastive learning steps, validated on synthetic and real-world data.

Contribution

ReCon is a novel, model-agnostic post-processing framework that significantly enhances community detection accuracy on signed networks through iterative refinement and contrastive learning.

Findings

ReCon consistently improves community detection accuracy across multiple methods.

ReCon is effective on both synthetic and real-world signed networks.

ReCon is easy to integrate with existing community detection algorithms.

Abstract

Community detection (CD) on signed networks is crucial for understanding how positive and negative relations jointly shape network structure. However, existing CD methods often yield inconsistent communities due to noisy or conflicting edge signs. In this paper, we propose ReCon, a model-agnostic post-processing framework that progressively refines community structures through four iterative steps: (1) structural refinement, (2) boundary refinement, (3) contrastive learning, and (4) clustering. Extensive experiments on eighteen synthetic and four real-world networks using four CD methods demonstrate that ReCon consistently enhances community detection accuracy, serving as an effective and easily integrable solution for reliable CD across diverse network properties.