A GRAPH CONVOLUTIONAL NETWORK APPROACH FOR PREDICTING NETWORK ROBUSTNESS

Xinbiao Lu (), Zecheng Liu, Hao Xing (), Xupeng Xie () and Chunlin Ye ()
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Xinbiao Lu: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Zecheng Liu: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Hao Xing: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Xupeng Xie: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Chunlin Ye: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China

Advances in Complex Systems (ACS), 2024, vol. 27, issue 07n08, 1-18

Abstract: Network robustness, which includes controllability robustness and connectivity robustness, reflects the ability of a network system to withstand attacks. In this paper, a Graph Convolutional Network (GCN) approach is proposed for predicting network robustness. In contrast to the existing Convolutional Neural Network (CNN) approach, the network topology and the node characteristics are directly used as GCN input without being converted into a grayscale image. Due to the reduction in the number of feature maps, the model size of a GCN is greatly reduced to only 1%Â of a CNN. Extensive experimental studies on four representative networks and six real networks have proven that the proposed approach can achieve better predictive performance with less training and running time.

Keywords: Complex network; robustness; graph convolutional network; prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S021952592550002X

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