Affiliations:
Software College, Northeastern University, Shenyang, P.R. China
Correspondence:
[*]
Corresponding author. Tao Ren and Miao-Miao Liu, Software College, Northeastern University, Shenyang, 110819, P.R. China. E-mails: chinarentao@163.com (T. Ren) and 1208521837@qq.com (M.-M. Liu).
Abstract: In this paper, the prediction of damage results for complex network is considered under grey information attack. Firstly, in order to construct more realistic networks, a new algorithm is proposed to generate 3 types of fully connected networks (normal scale-free network, scale-free network with cutoff, random network). Secondly, robustness of the 3 networks is analyzed under grey information attack. And then, a new method is proposed to predict the damage results by training the BP neural network. Thirdly, the effects of different topological parameters on the damage results are analyzed and a new method is proposed to find central nodes of the network. Finally, the damage results of a real bus network under grey information attack are predicted by the proposed method and several suggestions are given to help protect the real bus network.
Keywords: Complex network, damage result prediction, BP neural network, grey information attack, urban bus network
