用邻居分布提升图聚类，双中心优化更稳定可靠。
Dual-Center Graph Clustering with Neighbor Distribution
- 以邻居分布为监督信号，挖掘困难负样本
- 引入双中心分布，实现双重目标优化
- 适合需要高鲁棒性聚类的场景
图聚类对揭示复杂数据结构至关重要，但因其无监督特性面临挑战。近期基于目标导向的聚类方法表现优异，对比学习通过伪标签获得关注。然而伪标签作为监督信号不可靠，现有方法仅使用特征构建单中心分布进行单中心优化，导致引导不完整且不可靠。本文提出一种基于邻居分布特性的双中心图聚类（DCGC）方法，包含邻居分布表示学习与双中心优化。具体地，利用邻居分布作为监督信号，在对比学习中挖掘困难负样本，提升表示学习效果；同时引入邻居分布中心与特征中心，共同构建双目标分布用于双中心优化。大量实验与分析表明，该方法在性能与有效性上均显著优于现有方法。
原文摘要 · Abstract (English)
Graph clustering is crucial for unraveling intricate data structures, yet it presents significant challenges due to its unsupervised nature. Recently, goal-directed clustering techniques have yielded impressive results, with contrastive learning methods leveraging pseudo-label garnering considerable attention. Nonetheless, pseudo-label as a supervision signal is unreliable and existing goal-directed approaches utilize only features to construct a single-target distribution for single-center optimization, which lead to incomplete and less dependable guidance. In our work, we propose a novel Dual-Center Graph Clustering (DCGC) approach based on neighbor distribution properties, which includes representation learning with neighbor distribution and dual-center optimization. Specifically, we utilize neighbor distribution as a supervision signal to mine hard negative samples in contrastive learning, which is reliable and enhances the effectiveness of representation learning. Furthermore, neighbor distribution center is introduced alongside feature center to jointly construct a dual-target distribution for dual-center optimization. Extensive experiments and analysis demonstrate superior performance and effectiveness of our proposed method.
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