Robustness of higher-order network pertinent to first- and higher-order dependency

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Cuiping Ren, Bianbian Chen, Fengjie Xie
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引用次数: 0

Abstract

Robustness of higher-order network (HON) with dependency is different from first-order network (FON), as the attack orders and the number of iterations are totally different. To measure the robustness of HON with dependency, this study proposed new attack strategies on node and edge failure through measuring the size of the giant component and global efficiency. And two real-world systems of hazardous materials transportation accident causation system and Chinese high-speed railway system with different network size were empirically studied. Results show that HON is more fragile to robustness attacks than FON. The number of attack iterations is uncertain in HON, which depends on the attack strategies and the network structure. Meanwhile, nodes and edges with high dependency have greater impacts on network robustness, providing valuable insights into capturing critical information. This study may be helpful to design the robust system, improve the system's reliability and serve as a valuable reference for future exploration of HONs robustness.

高阶网络与一阶和高阶依赖性的相关性
具有依赖性的高阶网络(HON)的鲁棒性不同于一阶网络(FON),因为其攻击阶数和迭代次数完全不同。为了衡量带依赖性高阶网络的鲁棒性,本研究通过测量巨大分量的大小和全局效率,提出了新的节点和边缘故障攻击策略。并对网络规模不同的危险品运输事故致因系统和中国高速铁路系统这两个实际系统进行了实证研究。结果表明,HON 比 FON 更容易受到鲁棒性攻击。HON 的攻击迭代次数不确定,这取决于攻击策略和网络结构。同时,高依赖性的节点和边对网络鲁棒性的影响更大,这为捕捉关键信息提供了有价值的见解。本研究有助于设计鲁棒性系统,提高系统的可靠性,并为今后探索 HON 的鲁棒性提供有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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