Line graph contrastive learning for node classification

Abstract

Existing graph contrastive learning methods often rely on differences in node features within subgraphs, lacking effective capture of the global structural information of the graph. To address this issue, we propose a novel graph contrastive learning method, Line Graph Contrastive Learning (LineGCL), aimed at overcoming the deficiencies in understanding the overall characteristics and topological structures of graphs found in current methods. LineGCL utilizes the characteristics of line graphs to transform the original graph into corresponding line graphs, presenting edge information in the form of node features, effectively capturing the global structural information of the graph. Additionally, LineGCL adopts a novel multi-view contrastive learning approach, characterizing the similarity and differences between the original graph and line graph comprehensively from the perspectives of node features and spectral features, further enhancing the model's understanding and learning capabilities of the global structure of graphs. Experimental results on public datasets demonstrate that LineGCL outperforms baseline models, achieving performance improvements ranging from 0.5% to 28.5%. These results validate the effectiveness and superiority of LineGCL in capturing and understanding global structural information, surpassing the limitations of baseline methods in graph contrastive learning.

Keywords

• Graph contrastive learning
• Line graph
• Spectral features
• Multi-angle contrast loss