Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning
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.
Keywords
Cite
@article{arxiv.2601.16372,
title = {Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning},
author = {Hyunuk Shin and Hojin Kim and Chanyoung Lee and Yeon-Chang Lee and David Yoon Suk Kang},
journal= {arXiv preprint arXiv:2601.16372},
year = {2026}
}