AMGC2: Rethinking Deep Graph Clustering With a High Attribute-Missing Ratio.

Abstract

Attribute-missing deep graph clustering (DGC), which groups graph data containing samples with missing attributes, holds great potential to address the challenges of label scarcity and data absence in real-world scenarios. Among existing efforts, imputation-driven solutions (e.g., AMGC) employ the idea of classical imputation to tolerate missing attributes with the aid of prior clustering knowledge and show promising results. However, their abilities to conduct accurate imputation for clustering are impeded by the lack of ground truth of missing attributes, particularly in cases with high ratios of missing data. To tackle this issue, we propose a novel attribute-missing DGC framework called AMGC2, which leverages the prediction of intra-cluster feature distribution to enhance robustness in clustering and data completion at a high attribute-missing ratio. Specifically, we first encode observed attributes and structures separately and then entangle them into a common latent space. With the structure-attribute entangled embedding, we estimate the missing data by predicting the feature distribution within each cluster via Gaussian approximation rather than simply conducting feature similarity-based imputation using available attributes, such that the model can preserve intra-class compact signals for attribute-missing samples as well as boost the tolerance to a large proportion of data absence. Moreover, we perform cost-efficient prototype-level non-contrastive learning over completed samples, which in turn acquires more reliable sample partition prompts for distribution prediction in subsequent iterations. Extensive experiments verify the superior performance of AMGC2 against state-of-the-art competitors on seven datasets.