Self-Augmentation Graph Contrastive Learning for Multi-view Attribute Graph Clustering

Abstract

Multi-view attribute graph clustering is a fundamental task which aims to partition multi-view attributes into multiple clusters in an unsupervised manner. The existing multi-view attribute graph clustering methods lack the utilization of comprehensive structural information within each view and further ignore the unreliable relations between different views, leading to suboptimal clustering results. To this end, we develop a Self-Augmentation Graph Contrastive Learning (SAGCL) for multi-view attribute graph clustering, which integrates the comprehensive structural learning of view-specific and the alignment of multi-level reliable relations between different views into a unified framework. Graph self-augmentation strategy is proposed to adaptively explore the structural information within each view, which can comprehensively capture the critical structure of each view for multi-view attribute graph. Dual-alignment constraint is developed to guide the consistency of inter-view relations in the embedding-level and clustering-level, which can extract the consistent structure between multiple views and obtain cluster-oriented graph embedding with more discriminating. Furthermore, with the help of robust contrastive loss, our proposed network can suppress the existence of noisy information within each view and unreliable relations between different views. Extensive experiments prove that SAGCL outperforms the state-of-the-art methods.