Cascading dynamics on coupled networks with load-capacity interplay and concurrent recovery-failure

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-01 Epub Date: 2025-01-21 DOI:10.1016/j.physa.2025.130373
Jianwei Wang, Rouye He, Haozhe Sun, Haofan He
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Abstract

Coupled networks play a crucial role in modern infrastructure, where damage to one network can trigger cascading failures across the entire system. However, most studies on cascading failures in coupled networks have focused solely on failure propagation, overlooking the simultaneous occurrence of recovery and failure. To address this, we develop a general cascading failure model for interdependent networks that considers dynamic load-capacity interactions and concurrent recovery mechanisms. Specifically, the model captures how variations in the load of one network influence the coupled network and how recovery processes mitigate cascading effects. Using this model, we conducted experiments on a coupled power-communication network as a case study, employing various many-to-many coupling strategies. Results indicate disassortative coupling excels at low recovery thresholds, and assortative coupling at high thresholds, both outperforming random coupling and being less affected by recovery sensitivity. Larger node load differences resist random attacks better, while smaller differences resist maximum load attacks more effectively.
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负载-容量相互作用和并发恢复-故障耦合网络的级联动力学
耦合网络在现代基础设施中发挥着至关重要的作用,其中一个网络的损坏可能引发整个系统的级联故障。然而,大多数关于耦合网络中级联故障的研究只关注故障的传播,而忽略了恢复和故障的同时发生。为了解决这个问题,我们为相互依赖的网络开发了一个通用的级联故障模型,该模型考虑了动态负载能力交互和并发恢复机制。具体来说,该模型捕获了一个网络负载的变化如何影响耦合网络,以及恢复过程如何减轻级联效应。使用该模型,我们在耦合电力通信网络上进行了实验作为案例研究,采用了各种多对多耦合策略。结果表明,在低恢复阈值和高恢复阈值下,非分类耦合优于随机耦合,且受恢复灵敏度的影响较小。节点负载差越大,抗随机攻击能力越强,节点负载差越小,抗最大负载攻击能力越强。
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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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