Multi-view fair-augmentation contrastive graph clustering with reliable pseudo-labels

Abstract

Graph contrastive clustering (GCC) has achieved numerous advantageous results due to the information mining capability of self-supervised learning. Multi-view attribute graph clustering, as a means of addressing complex attribute graph data with multi-view relations or features, has gained significant attention in recent years. However, only a few GCC methods are applicable to multi-view graph data, limiting the full utilization of the rich information contained in such data. Furthermore, most GCC methods rely on random augmentation strategies, leading to structural unfairness in the augmentation process across views. To address these issues, we propose multi-view fair-augmentation contrastive graph clustering with reliable pseudo-labels (MFCGC), which combines contrastive learning and fully utilizes the node-level and cluster-level information within each view of the multi-view data, ensuring the consistency of information across different views. Moreover, we designed a carefully crafted node-degree-based augmentation method called fair augmentation, which preserves the partial topology structure of multi-view data. Finally, we propose a reliable pseudo-label selection mechanism that integrates reliable pseudo-labels from multi-view graph to improve the quality of sample pairs. Our proposed MFCGC has been extensively evaluated on real-world datasets, demonstrating its superiority over state-of-the-art algorithms in two categories of multi-view graph data and attesting to its significant effectiveness.