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Multi-View Enhancement Graph-Level Clustering Network
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DOI：10.1109/ACCESS.2025.3550185.png)
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Abstract
En 中文
Graph-level clustering is a fundamental and significant task in data mining. The advancement of graph neural networks has provided substantial impetus to this area of research. However, existing graph-level clustering methods often focus exclusively on either graph structure or node attributes, which limit their ability to comprehensively capture graph-level features. To address this issue, we propose a Multi-view Enhancement Graph-level Clustering Network (ME-GCN), which consists of a Multi-view Feature Extraction Strategy (MFES) and a Graph-level Heterogeneous Enhancement Mechanism (GHEM) to generate high-quality graph-level representations, thus improving clustering performance. Specifically, the network extracts node attributes, subgraph structure, and global structure information through three personalized encoders with different receptive fields, respectively, to enrich feature representations from different views. In addition, it perceives and reasons heterogeneous features through GHEM, including multi-source information and hierarchical enhancement, which improves the compactness of multi-source representations. Extensive experiments on five benchmark datasets have demonstrated the superiority of ME-GCN, highlighting its effectiveness in leveraging multiple types of structure information for deep graph-level clustering.
Keywords:
Feature extraction
Accuracy
Contrastive learning
Periodic structures
Noise
Kernel
Data mining
Topology
Robustness
Deep graph learning
graph-level clustering
graph neural networks
unsupervised learning
unsupervised learning