Research on key node identification of complex network based on GCN

Abstract:

Accurately identifying the key nodes in the network is one of the important research topics in complex networks. Most of the existing key node identification methods are based on the centrality measurement method by the network structure, which has low identification accuracy and limited scope of application. A key node identification method, based on Graph Convolutional Network (GCN), is proposed in this paper, which considers not only the node attributes, but also the network structure and neighbor node structure. Multidimensional features are extracted first from the network legend data to construct feature vectors and then the node feature vector is input to the GCN layer for learning. Finally, the minimum loss is calculated with the regression loss function, and the key nodes are identified. In this paper, SIR (Susceptible Infected Removed) is choosed as the evaluation method in the propagation dynamics simulation experiment and Pinning Control experiment, the proposed method is verified on different types of real networks, the results show that the GCNbased method proposed in this paper outperforms other methods in terms of scope of application and accuracy.