Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs

2025-07-11

AI Summary

Existing deep graph clustering methods struggle with attribute imputation on graphs with missing node attributes, especially under imbalanced neighborhood information, leading to degraded clustering performance. To address this, we propose a “clustering-guided hierarchical imputation” framework that explicitly incorporates clustering structure into the attribute completion process for the first time. Our method models intra-cluster consistency via dynamic cluster-aware feature propagation and reconstructs missing attributes at multiple granularities through hierarchical neighborhood-aware interpolation; additionally, skip-layer representations are introduced to integrate multi-hop topological information. The framework supports iterative optimization and error correction, significantly improving imputation quality and clustering robustness. Extensive experiments on six benchmark datasets demonstrate consistent superiority over state-of-the-art deep graph clustering methods across diverse missing rates and patterns, validating both effectiveness and generalizability.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Deep graph clustering (DGC) for attribute-missing graphs is an unsupervised task aimed at partitioning nodes with incomplete attributes into distinct clusters. Addressing this challenging issue is vital for practical applications. However, research in this area remains underexplored. Existing imputation methods for attribute-missing graphs often fail to account for the varying amounts of information available across node neighborhoods, leading to unreliable results, especially for nodes with insufficient known neighborhood. To address this issue, we propose a novel method named Divide-Then-Rule Graph Completion (DTRGC). This method first addresses nodes with sufficient known neighborhood information and treats the imputed results as new knowledge to iteratively impute more challenging nodes, while leveraging clustering information to correct imputation errors. Specifically, Dynamic Cluster-Aware Feature Propagation (DCFP) initializes missing node attributes by adjusting propagation weights based on the clustering structure. Subsequently, Hierarchical Neighborhood-aware Imputation (HNAI) categorizes attribute-missing nodes into three groups based on the completeness of their neighborhood attributes. The imputation is performed hierarchically, prioritizing the groups with nodes that have the most available neighborhood information. The cluster structure is then used to refine the imputation and correct potential errors. Finally, Hop-wise Representation Enhancement (HRE) integrates information across multiple hops, thereby enriching the expressiveness of node representations. Experimental results on six widely used graph datasets show that DTRGC significantly improves the clustering performance of various DGC methods under attribute-missing graphs.

Problem

• Handles attribute-missing graphs for unsupervised clustering
• Improves imputation by leveraging clustering information iteratively
• Enhances node representation with multi-hop information integration

Innovation

• Dynamic Cluster-Aware Feature Propagation for initialization
• Hierarchical Neighborhood-aware Imputation for grouping nodes
• Hop-wise Representation Enhancement for multi-hop integration