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. The code of RAGC could be available at https://github.com/TianxiangZhao0474/RAGC.git.

Attributed graph clustering with auto-encoder (AE) and graph convolutional network (GCN) has achieved promising performance by fusing node attribute feature and structural graph information. However, there are some limitations: (i) structural information from pre-defined graph is inaccurate and insufficient for graph representation learning; (ii) graph embedding of last layer only contains partial information for clustering which inevitably deteriorates clustering performance. To address these issues, we propose the Improved Attributed Graph Clustering method with Representation and Structure Augmentation (IAGC-RSA). The representation augmentor with multi-scale and multi-source representation attention fusion and structure augmentor with adaptive graph learning are designed for information augmentation from structure level and feature level. Thus, IAGC-RSA could learn a more comprehensive and discriminative graph embedding representation for subsequent clustering task. Experimental results conducted on some benchmark datasets demonstrate the effectiveness of IAGC-RSA for node clustering task.