Graph Neural Network （GNN） is an effective graph representation learning method for processing graph structure data. However， the performance of GNN in practical applications is limited by the problem of missing information. On the one hand， the graph structure is usually sparse， making it difficult for the model to learn node features adequately. On the other hand， model training is limited because supervised learning relies on sparse label data， making it difficult to obtain robust node representations. To address these problems， a Subgraph-aware Contrastive Learning with Data Augmentation （SCLDA） model was proposed. Firstly， the relationship scores among nodes were obtained by learning the original graph through link prediction， and the edges with the highest scores were added to the original graph to generate the enhanced graph. Secondly， local subgraphs of the original and enhanced graphs were sampled by using target nodes respectively， and the target nodes of subgraphs were input to the shared GNN encoder， so as to generate the target node embeddings at subgraph level. Finally， the mutual information between similar instances was maximized on the basis of contrastive learning of the target nodes from the two perspective subgraphs. Experimental results of node classification on six public datasets Cora， Citeseer， Pubmed， Cora_ML， DBLP， and Photo show that SCLDA model improves the accuracy over the traditional GCN model by about 4.4%， 6.3%， 4.5%， 7.0%， 13.2% and 9.3%， respectively.
