DCMSL: Dual influenced community strength-boosted multi-scale graph contrastive learning

Graph Contrastive Learning (GCL) effectively mitigates label dependency, defining positive and negative pairs for node embeddings. Nevertheless, most GCL methods, including those considering communities, overlooking the simultaneous influence of community and node—a crucial factor for accurate embeddings. In this paper, we propose Dual influenced Community Strength-boosted Multi-Scale Graph Contrastive Learning (DCMSL), concurrently considering community and node influence for comprehensive contrastive learning. Firstly, we define dual influenced community strength which can be adaptable to diverse datasets. Based on it, we define node cruciality to differentiate node importance. Secondly, two graph data augmentation methods, NCNAM and NCED, respectively, are put forward based on node cruciality, guiding graph augmentation to preserve more influential semantic information. Thirdly, a joint multi-scale graph contrastive scheme is raised to guide the graph encoder to learn data semantic information at two scales: (1) Propulsive force node-level graph contrastive learning—a node-level graph contrastive loss defining the force to push negative pairs in GCL farther away. (2) Community-level graph contrastive learning—enabling the graph encoder to learn from data on the community level, improving model performance. DCMSL achieves state-of-the-art results, demonstrating its effectiveness and versatility in two node-level tasks: node classification and node clustering and one edge-level task: link prediction. Our code is available at: https://github.com/HanChen-HUST/DCMSL.