Robustness of edge-coupled interdependent networks with reinforced edges

IF 2.2 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Junjie Zhang, Caixia Liu, Shuxin Liu, Fei Pan, Weifei Zang
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引用次数: 0

Abstract

Abstract Previous studies on cascade failures in interdependent networks have mainly focused on node coupling relationships. However, in realistic scenarios, interactions often occur at the edges connecting nodes rather than at the nodes themselves, giving rise to edge-coupled interdependent networks. In this article, we extend the model of partially edge-coupled interdependent networks by introducing reinforced edges with a ratio of ρ. We analyse the formation of finite surviving components in edge-coupled networks, wherein the reinforced edges can function and support their neighbouring nodes to form functional components. To accomplish this, we develop a framework through a detailed mathematical derivation of the proposed model. We then investigate the critical value ρ* of the reinforced edge ratio that can change the phase transition type of the network. Our model is verified by theoretical analysis, simulation experiments and real network systems. The results show that the introduction of a small proportion of reinforced edges in the edge-coupled interdependent network can avoid the sudden collapse of the network and significantly improve the robustness of the network.
带增强边的边耦合相互依赖网络的鲁棒性
摘要以往对相互依赖网络中级联故障的研究主要集中在节点耦合关系上。然而,在现实场景中,交互通常发生在连接节点的边缘,而不是节点本身,从而产生了边缘耦合的相互依赖网络。在本文中,我们扩展了部分边耦合相互依赖网络的模型,引入了具有ρ比率的增强边。我们分析了边缘耦合网络中有限幸存组件的形成,其中增强的边缘可以发挥作用并支持其邻近节点形成功能组件。为了实现这一点,我们通过对所提出的模型进行详细的数学推导来开发一个框架。然后,我们研究了可以改变网络相变类型的增强边比的临界值ρ*。理论分析、仿真实验和实际网络系统验证了模型的正确性。结果表明,在边耦合相互依赖网络中引入小比例的增强边可以避免网络的突然崩溃,显著提高网络的鲁棒性。
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来源期刊
Journal of complex networks
Journal of complex networks MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.20
自引率
9.50%
发文量
40
期刊介绍: Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network
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