Node classification methods of complex network are mostly realized based on node representation learned by the graph neural network, the graph neural network encodes local structure information of complex networks through neighborhood aggregation. However, the over-smoothing problem of the graph neural network limits the node classification performance of complex network. In view of this problem, a node classification method of complex networks based on enhanced graph neural networks and contrastive learning was proposed. In the proposed method, not only the attention was introduced to the neighborhood nodes, in order to differentiate the importance of each neighbor node, but also the feature of each edge was constructed with combination of the local neighborhood overlap and the global neighborhood overlap, so as to expand the information of the node representation. Finally, contrastive learning was introduced to train the neural networks, so that the network’s global node priori information was utilized to jointly optimize the node representation. Experiments were performed on Cora, Citeseer, PubMed and Chameleon public network datasets. The results demonstrate that compared to the other advanced methods, the proposed method achieves better node classification performance, moreover, the effectiveness of the proposed method is verified through ablation study
