MTA-BME Information Systems Research Group, Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, H-1117, Budapest, Magyar tudósok, krt. 2, Hungary.
Federal state budgetary institution of science, Institute of Applied Physics of the Russian Academy of Sciences, 46 Ul'yanov Street, 603950, Nizhny Novgorod, Russia.
Sci Rep. 2017 May 11;7(1):1730. doi: 10.1038/s41598-017-01824-y.
The rich club organization (the presence of highly connected hub core in a network) influences many structural and functional characteristics of networks including topology, the efficiency of paths and distribution of load. Despite its major role, the literature contains only a very limited set of models capable of generating networks with realistic rich club structure. One possible reason is that the rich club organization is a divisive property among complex networks which exhibit great diversity, in contrast to other metrics (e.g. diameter, clustering or degree distribution) which seem to behave very similarly across many networks. Here we propose a simple yet powerful geometry-based growing model which can generate realistic complex networks with high rich club diversity by controlling a single geometric parameter. The growing model is validated against the Internet, protein-protein interaction, airport and power grid networks.
富俱乐部组织(网络中高度连接的枢纽核心的存在)影响许多网络的结构和功能特性,包括拓扑结构、路径效率和负载分布。尽管富俱乐部组织具有重要作用,但文献中仅有非常有限的一些模型能够生成具有真实富俱乐部结构的网络。一个可能的原因是,富俱乐部组织是复杂网络中具有分裂性的属性,它们表现出很大的多样性,与其他指标(例如直径、聚类或度分布)形成鲜明对比,这些指标在许多网络中似乎表现得非常相似。在这里,我们提出了一种简单而强大的基于几何的增长模型,该模型可以通过控制单个几何参数来生成具有高富俱乐部多样性的真实复杂网络。该增长模型通过与互联网、蛋白质-蛋白质相互作用、机场和电网网络进行对比得到验证。