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

arXiv: https://arxiv.org/abs/2601.16372 · HTML: https://mlsanity.com/paper/2601.16372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [SPONGE: A generalized eigenproblem for clustering signed networks](https://mlsanity.com/paper/1904.08575/) (2019)
- [SSSNET: Semi-Supervised Signed Network Clustering](https://mlsanity.com/paper/2110.06623/) (2021)
- [Robust Deep Signed Graph Clustering via Weak Balance Theory](https://mlsanity.com/paper/2502.05472/) (2025)

## Abstract (arXiv)

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.