Improving Graph Contrastive Learning with Community Structure

Graph contrastive learning (GCL) has demonstrated remarkable success in training graph neural networks (GNNs) by distinguishing positive and negative node pairs without human labeling. However, existing GCL methods often suffer from two limitations: the repetitive message-passing mechanism in GNNs and the quadratic computational complexity of exhaustive node pair sampling in loss function. To address these issues, we propose an efficient and effective GCL framework that leverages community structure rather than relying on the intricate node-to-node adjacency information. Inspired by the concept of sparse low-rank approximation of graph diffusion matrices, our model delivers node messages to the corresponding communities instead of individual neighbors. By exploiting community structures, our method significantly improves GCL efficiency by reducing the number of node pairs needed for contrastive loss calculation. Furthermore, we theoretically prove that our model effectively captures essential structure information for downstream tasks. Extensive experiments conducted on real-world datasets illustrate that our method not only achieves the state-of-the-art performance but also substantially reduces time and memory consumption compared with other GCL methods. Our code is available at [https: //github. com/chenx-hi/IGCL-CS](https: //github. com/chenx-hi/IGCL-CS).

Authors

• Xiang Chen
• Kun Yue
• Liang Duan
• Lixing Yu

Keywords

• Graph contrastive learning
• Graph neural network
• Community structure
• Graph partition
• Community contrastive loss