Yunda Wang , Yongxiang Xia , Qinfa Xu , Jingrui Wang , Zhen Wang
{"title":"Cascading failures in multiple-to-multiple interdependent networks considering interdependent failure threshold","authors":"Yunda Wang , Yongxiang Xia , Qinfa Xu , Jingrui Wang , Zhen Wang","doi":"10.1016/j.physa.2025.130712","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, the study of interdependent networks has emerged as a focus within the field of complex networks. In analyses of the robustness of networks with multiple support-dependent relationships, traditional interdependent failure rules often assume that a node fails when one or all of its interdependent nodes fail. Such assumptions often fail to capture the probabilistic nature of real-world network failures. More generally, interdependent networks in the real world typically exhibit failure dynamics in which a node fails if a certain proportion of its interdependent nodes fail. To address this fact, this paper proposes a model of multiple-to-multiple interdependent networks that introduces an interdependent failure threshold, defined as the proportion of failed interdependent nodes required to trigger the failure of a given node. The study investigates how the varying number of interdependent edges and the interdependent failure threshold influence the robustness of the network under random or intentional attacks. In intentional attacks, the robustness of the network decreases as the number of interdependent links increases. Additionally, assortatively coupled networks demonstrate greater robustness under random attacks, while randomly coupled networks are more robust under intentional attacks. Finally, we verify the correctness of our results in real-world settings and obtain conclusions that are basically consistent with the theoretical model. These findings provide valuable information on the resilience mechanisms of interdependent networks in the real world, which has implications for the design of more robust systems.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"674 ","pages":"Article 130712"},"PeriodicalIF":2.8000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437125003644","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, the study of interdependent networks has emerged as a focus within the field of complex networks. In analyses of the robustness of networks with multiple support-dependent relationships, traditional interdependent failure rules often assume that a node fails when one or all of its interdependent nodes fail. Such assumptions often fail to capture the probabilistic nature of real-world network failures. More generally, interdependent networks in the real world typically exhibit failure dynamics in which a node fails if a certain proportion of its interdependent nodes fail. To address this fact, this paper proposes a model of multiple-to-multiple interdependent networks that introduces an interdependent failure threshold, defined as the proportion of failed interdependent nodes required to trigger the failure of a given node. The study investigates how the varying number of interdependent edges and the interdependent failure threshold influence the robustness of the network under random or intentional attacks. In intentional attacks, the robustness of the network decreases as the number of interdependent links increases. Additionally, assortatively coupled networks demonstrate greater robustness under random attacks, while randomly coupled networks are more robust under intentional attacks. Finally, we verify the correctness of our results in real-world settings and obtain conclusions that are basically consistent with the theoretical model. These findings provide valuable information on the resilience mechanisms of interdependent networks in the real world, which has implications for the design of more robust systems.
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.