面向鲁棒属性图聚类的混合协同增强与对比样本自适应差分感知
Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering
AI总结：
针对现有对比属性图聚类忽略边级增强及样本对差异的问题，提出融合混合协同增强与对比样本自适应差分感知的鲁棒聚类方法，在六个基准数据集上性能优于现有最优方法。
摘要：
对比属性图聚类（CAGC）凭借其强大的自监督表示学习与聚类能力已取得巨大成功，该方法的效果主要依赖有效的数据增强与对比目标设定。然而，大多数CAGC方法将边作为辅助信息来获取节点级嵌入表示，且仅关注节点级嵌入增强，这种方式忽略了边级嵌入增强，以及不同粒度下节点级与边级嵌入增强之间的交互作用。此外，这类方法通常对所有对比样本对一视同仁，忽视了难、易正负样本对之间的显著差异，最终限制了模型的判别能力。为解决这些问题，本文提出了一种融合混合协同增强（HCA）与对比样本自适应差分感知（CSADA）的新型鲁棒属性图聚类方法（RAGC）。首先，该方法同时执行节点级与边级嵌入表示及增强操作，为后续对比学习建立更全面的相似性度量准则；反过来，具备判别性的相似性会进一步有意识地引导边增强过程。其次，利用高置信度的伪标签信息，本文精心设计了CSADA策略，该策略可自适应识别所有对比样本对，并通过一种创新的权重调制函数对其进行差异化处理。HCA与CSADA模块在良性循环中相互强化，从而提升表示学习的判别性。在六个基准数据集上开展的全面图聚类评估结果表明，所提出的RAGC方法相较于多种当前最优的CAGC方法具备更优的性能。
英文摘要：
Due to its powerful capability of self-supervised representation learning and clustering, contrastive attributed graph clustering (CAGC) has achieved great success, which mainly depends on effective data augmentation and contrastive objective setting. However, most CAGC methods utilize edges as auxiliary information to obtain node-level embedding representation and only focus on node-level embedding augmentation. This approach overlooks edge-level embedding augmentation and the interactions between node-level and edge-level embedding augmentations across various granularity. Moreover, they often treat all contrastive sample pairs equally, neglecting the significant differences between hard and easy positive-negative sample pairs, which ultimately limits their discriminative capability. To tackle these issues, a novel robust attributed graph clustering (RAGC), incorporating hybrid-collaborative augmentation (HCA) and contrastive sample adaptive-differential awareness (CSADA), is proposed. First, node-level and edge-level embedding representations and augmentations are simultaneously executed to establish a more comprehensive similarity measurement criterion for subsequent contrastive learning. In turn, the discriminative similarity further consciously guides edge augmentation. Second, by leveraging pseudo-label information with high confidence, a CSADA strategy is elaborately designed, which adaptively identifies all contrastive sample pairs and differentially treats them by an innovative weight modulation function. The HCA and CSADA modules mutually reinforce each other in a beneficent cycle, thereby enhancing discriminability in representation learning. Comprehensive graph clustering evaluations over six benchmark datasets demonstrate the effectiveness of the proposed RAGC against several state-of-the-art CAGC methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
