Mutual-View Contrastive Generative Framework for Attribute-Missing Graph Clustering

Abstract

Attribute-Missing Graph Clustering addresses the challenging problem of incomplete node attribute information in graphs. Recent advancements in self-supervised learning techniques, particularly contrastive learning and generative approaches, have shown effectiveness in tackling tasks involving missing attributes in graphs. However, many existing methods rely on mask-based view augmentation, which provides limited information gain for contrastive learning and falls short in fully reconstructing missing information in generative models, leading to suboptimal performance. To address these limitations, we propose the Mutual-View Contrastive Generative Framework (MVCG). This framework constructs two equivalent views to enhance both the contrastive and generative components. Our framework features a Contrastive Cluster-Refinement Mechanism that generates pseudo-labels for cross-view imputation and cluster refinement, and an Asymmetric Generative Mechanism that reconstructs both structural and attribute information. Extensive experiments on multiple benchmark datasets demonstrate that MVCG outperforms existing methods in four clustering metrics. The code for MVCG is available at https://github.com/colaudiolab/MVCG.