用自洽和桥公理选择中心性测度

IF 2.2 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Pavel Chebotarev
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引用次数: 2

摘要

摘要考虑了几种由图核引起的网络中心性度量,其中包括一些已知的度量和许多新的度量。早在文献中出现的自洽公理和桥公理与某些核和其中一个族密切相关。我们得到了自洽的一个充分必要条件和桥公理的一个充分条件,指出了满足这些公理的具体测度,并证明了在某些附加条件下它们是不相容的。应用于无向网络的PageRank中心性违反了研究中的大多数条件,并且根据一些作者的说法,中心性度量具有“难以想象”的性质。我们解释这种现象。采用自洽或桥公理导致在挑选最合适的中心性措施的剔除方法中调查时间的急剧减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selection of centrality measures using Self-consistency and Bridge axioms
Abstract We consider several families of network centrality measures induced by graph kernels, which include some well-known measures and many new ones. The Self-consistency and Bridge axioms, which appeared earlier in the literature, are closely related to certain kernels and one of the families. We obtain a necessary and sufficient condition for Self-consistency, a sufficient condition for the Bridge axiom, indicate specific measures that satisfy these axioms and show that under some additional conditions they are incompatible. PageRank centrality applied to undirected networks violates most conditions under study and has a property that according to some authors is ‘hard to imagine’ for a centrality measure. We explain this phenomenon. Adopting the Self-consistency or Bridge axiom leads to a drastic reduction in survey time in the culling method designed to select the most appropriate centrality measures.
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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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