GCL-GCN: Graphormer and Contrastive Learning Enhanced Attributed Graph Clustering Network

2025-07-25

AI Summary

Addressing challenges in attributed graph clustering—namely, insufficient local dependency modeling and difficulty capturing global structure due to graph sparsity and node attribute heterogeneity—this paper proposes a deep graph clustering framework integrating global and local representation learning. Its core contributions are: (1) a centrality-enhanced spatial attention Graphormer module that jointly encodes node centrality and spatial relationships, explicitly modeling both long-range dependencies and local neighborhood structures; and (2) a contrastive learning-based two-stage pretraining strategy to enhance node representation discriminability and clustering robustness. The framework jointly optimizes representation learning and clustering objectives in an end-to-end manner. Extensive experiments on six benchmark datasets demonstrate significant improvements over 14 state-of-the-art methods: on Cora, it achieves absolute gains of 4.94% in ACC, 13.01% in NMI, and 10.97% in ARI, validating its effectiveness and stability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Attributed graph clustering holds significant importance in modern data analysis. However, due to the complexity of graph data and the heterogeneity of node attributes, leveraging graph information for clustering remains challenging. To address this, we propose a novel deep graph clustering model, GCL-GCN, specifically designed to address the limitations of existing models in capturing local dependencies and complex structures when dealing with sparse and heterogeneous graph data. GCL-GCN introduces an innovative Graphormer module that combines centrality encoding and spatial relationships, effectively capturing both global and local information between nodes, thereby enhancing the quality of node representations. Additionally, we propose a novel contrastive learning module that significantly enhances the discriminative power of feature representations. In the pre-training phase, this module increases feature distinction through contrastive learning on the original feature matrix, ensuring more identifiable initial representations for subsequent graph convolution and clustering tasks. Extensive experimental results on six datasets demonstrate that GCL-GCN outperforms 14 advanced methods in terms of clustering quality and robustness. Specifically, on the Cora dataset, it improves ACC, NMI, and ARI by 4.94%, 13.01%, and 10.97%, respectively, compared to the primary comparison method MBN.

Problem

• Enhancing attributed graph clustering with local and global dependencies
• Improving feature distinction via contrastive learning in graph data
• Addressing sparse and heterogeneous graph data clustering challenges

Innovation

• Graphormer module combines centrality and spatial relationships
• Contrastive learning enhances feature representation discriminative power
• Pre-training ensures identifiable initial representations for clustering