1. School of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, China 2. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China 3. Marketing Service Center of Jiangsu Electric Power Co. Ltd, Nanjing 210019, China 4. Maanshan University, Maanshan 243100, China
The importance measure of nodes in complex networks is correlated with the time attribute. The classical static network model weakens the effective representation of the time attribute of node interaction. A node importance evaluation model for temporal networks based on the graph convolution union computing was proposed. The model migrated the deep learning to dynamic graph data for end-to-end system modeling. Dynamic evolution process of the temporal network structure was assembled by the supra-adjacency matrix. The graph convolutional neural network framework was used to calculate the fusion characteristics of the neighborhood nodes. The node importance order structure over time was analyzed. A comprehensive ranking of node importance was achieved. The simulation experimental results showed that compared with the existing method, the Kendall’s tau values obtained by the proposed method performed well on all the selected network datasets, reflecting the effectiveness and superiority of the proposed method.
Chuan-hua ZHOU,Li-chun CAO,Jia-yi ZHOU,Feng ZHAN. Identification of critical nodes in temporal networks based on graph convolution union computing. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 930-938.
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