Contrastive deep graph clustering with hard boundary sample awareness

Abstract

Contrastive deep graph clustering is a graph data clustering method that combines deep learning and contrastive learning, aiming to realize accurate clustering of graph nodes. Although existing hard sample mining-based methods show positive results, they face the following challenges: (1) Clustering methods based on data similarity may lose important intrinsic groupings and association patterns, thereby affecting a comprehensive understanding and interpretation of the data. (2) In the measurement of hard samples, ignoring hard boundary samples may exacerbate clustering bias. To address these issues, we propose a new contrastive deep graph clustering method called Hard Boundary Sample Aware Network (HBSAN), which introduces attribute and structure enhanced encoding and generalized dynamic hard boundary sample weighting modulation strategy. Specifically, we optimize the similarity computation among samples through adaptive attribute embedding and multiview structure embedding techniques to deeply explore the intrinsic connections among samples, thereby aiding in the measurement of hard boundary samples. Furthermore, we leverage the unreliable confidence information obtained from initial clustering analysis to design an innovative hard boundary sample weight modulation function. This function first identifies the hard boundary samples and then dynamically reduces their weights, effectively enhancing the discriminative capability of network in ambiguous classification scenarios. Combining extensive experimental evaluations and in-depth analysis, our approach achieves state-of-the-art performance and establishes superior results in handling complex network clustering tasks.