通过四步迭代优化，提升有符号网络社区发现的准确性。
Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning
- 四步迭代修正：结构、边界、对比学习、聚类
- 在22个数据集上显著提升4种方法的检测精度
- 不依赖模型，可无缝集成到现有算法中
有符号网络中的社区发现对理解正负关系如何共同塑造网络结构至关重要。然而，现有方法常因边符号噪声或冲突导致社区不一致。本文提出ReCon，一种模型无关的后处理框架，通过四个迭代步骤逐步优化社区结构：(1) 结构精炼，(2) 边界精炼，(3) 对比学习，(4) 聚类。在18个合成网络和4个真实世界网络上，使用4种社区发现方法进行大量实验表明，ReCon能持续提升社区检测准确率，是一种有效且易于集成的可靠解决方案，适用于多种网络特性。
原文摘要 · Abstract (English)
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.
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