Abstract
The robustness and resilience of complex networks have been widely studied and discussed in both research and industry because today, the diversity of system components and the complexity of the connection between units are increasingly influencing the reliability of complex systems. Previous studies have focused on node failure in networks, proposing several performance indicators. However, a single performance indicator cannot comprehensively measure all the performance aspects; thus, the selected performance indicators and recovery strategies lack consistency with respect to the targeted complex systems. This paper introduces a novel stress–strength-balanced weighted network model based on two network transmission hypotheses. Then, with respect to different concerns of the complex network, we propose two modified network performance measurement indicators and compare these indicators in terms of their trends in the attack process. In addition, we introduce several network recovery strategies and compare their efficiencies. Our findings are as follows: (1) The evaluation and judgment of the network performance depend on the performance measurement indicators we use. (2) Different recovery strategies exhibit distinct efficiencies in recovering different aspects of network performance, and no strategy exists that can improve all the network performance aspects simultaneously. (3) The timing of the recovery is proved to have a deep influence on the cost and efficiency of network recovery; thus, the optimal recovery strategy for a damaged network varies with the extent of the damage. From the results of the simulation of the attack-recovery process, we conclude that while defining and analyzing complex network models, we should adjust our network topology, weight assignment, and performance indicators in accordance with the focal characteristics of complex systems so that we can use the network model to build robust complex systems and efficient logistics and maintenance strategies.
Highlights
Since the end of the 20th century, the notion of a network has attracted huge attention in scientific research
(3) The timing of the recovery is proved to have a deep influence on the cost and efficiency of network recovery; the optimal recovery strategy for a damaged network varies with the extent of the damage
We propose a stress–strength-balanced BA network model, which corresponds to real-life complex networks in terms of their growth and self-adaptive organizing characteristics
Summary
Editor: Lidia Adriana Braunstein, Universidad Nacional de Mar del Plata, ARGENTINA. Data Availability Statement: All relevant data can be reproduced by the algorithms within the paper. Nsfc.gov.cn/] and the National Key Research and Development Program [http://program.most.gov. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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