增长方法的普遍行为及局部枢纽在级联失效中的重要性

IF 2.2 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Wonhee Jeong;Unjong Yu
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引用次数: 0

摘要

我们引入了集线器中心性,并研究了集线器中心度与网络中每个节点的程度之间的关系。我们在增长方法生成的各种网络中发现并验证了它们之间的普遍关系,但由于丰富的俱乐部现象和本地集线器的存在,这种关系不适用于现实世界的网络。通过对目标攻击和过载级联故障的研究,我们证明了集线器中心性是一个有意义的参数,它在现实世界的网络中提供了超越程度的额外见解。特别是,我们证明了本地集线器在现实网络中占据关键位置,发生全局级联故障的概率更高。因此,我们得出结论,增长方法生成的网络不包括本地集线器,在描述真实世界的网络时不可避免地存在局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Universal behaviour of the growth method and importance of local hubs in cascading failure
We introduce hub centrality and study the relation between hub centrality and the degree of each node in the networks. We discover and verify a universal relation between them in various networks generated by the growth method, but the relation is not applied to real-world networks due to the rich-club phenomenon and the presence of local hubs. Through the study of a targeted attack and overload cascading failure, we prove that hub centrality is a meaningful parameter that gives extra insight beyond degree in real-world networks. Especially, we show that the local hubs occupy key positions in real-world networks with higher probabilities to incur global cascading failure. Therefore, we conclude that networks generated by the growth method, which do not include local hubs, have inevitable limitations to describe real-world networks.
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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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