A Node Importance Prediction Algorithm Based on Graph Attention and Contrastive Learning

Abstract In complex network analysis, node ranking is vital for propagation prediction, structural optimization, and intervention strategy design, yet existing methods often fail to effectively integrate community information in dynamic settings. To address this, this paper proposes a node ranking method that combines graph attention mechanisms with contrastive learning. Community detection is employed to extract node-level community features, and a joint embedding module is designed to fuse global and local structures, thereby incorporating community information into node representations. Based on this, a multi-layer graph attention network adaptively learns node and neighborhood features, while contrastive learning mitigates interference from dynamic evolution and strengthens the model’s ability to capture multi-scale structural differences. Experiments on multiple dynamic network datasets show that the proposed method significantly outperforms existing approaches in ranking accuracy, particularly in networks with higher average degrees and clearer community structures. These results validate the effectiveness of the method in enhancing feature representation and modeling multi-scale dynamic node influence.