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摘要
Based on the nearest neighbor information of the node, the resilience of complex systems can be measured by using prediction model for complex system resilience through mapping multidimensional equation into one-dimensional equation. However, this model does not introduce the second-order neighbor information of the node. In this paper, we present a prediction model of the resilience of complex systems by considering the second-order neighbor information. Then using the Barabási-Albert (BA) scale free network and Watts-Strogatz (WS) small world network, we investigate the effect of improved model and explore the impact of improved model with different network structures. The experiment results show that for the BA scale free network and WS small-world network with different average degree of network, the improved model considering with the second-order neighbor information can predict the resilience of complex systems more accurately. When the average degrees of the BA scale free network and WS small-world network are 2, the accuracies of system resilience measurement are increased by 79.89% and 59.53%. For the same kind of networks the smaller the average degree of networks is, the more accurate the prediction of the improved model is. Then we also find that when the size and average degree of networks are the same, the effect of improved model is better for BA scale-free network than WS small-word network. Our researches provide theoretical support and research method for measuring resilience of complex networks and designing resilient systems. ? 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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