Random graphs with arbitrary clustering and their applications.
Random graphs with arbitrary clustering and their applications.
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DOI：
10.1103/physreve.103.012309
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
P. Mann;V. Smith;J. Mitchell;S. Dobson
中科院分区：
文献类型：
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作者：
P. Mann;V. Smith;J. Mitchell;S. Dobson
许多真实网络的结构不是局部树状的，因此，网络分析无法表征它们的键渗透特性。在最近的一篇论文中[P.]Mann， V. A. Smith， J. B. O. Mitchell， S. Dobson， J. B. O. Mitchell， J. B. O. Dobson， J. B. O. Dobson， J. B. O. A. [j] .中国科学：自然科学，2009,(6):444。在本文中，我们将该模型扩展到包含节点度不相等的簇的网络，包括多层网络。通过数值算例，我们展示了该方法如何用于研究具有任意聚类的随机复杂网络的性质，扩展了组态模型和生成函数公式的适用性。
The structure of many real networks is not locally treelike and, hence, network analysis fails to characterize their bond percolation properties. In a recent paper [P. Mann, V. A. Smith, J. B. O. Mitchell, and S. Dobson, arXiv:2006.06744], we developed analytical solutions to the percolation properties of random networks with homogeneous clustering (clusters whose nodes are degree equivalent). In this paper, we extend this model to investigate networks that contain clusters whose nodes are not degree equivalent, including multilayer networks. Through numerical examples, we show how this method can be used to investigate the properties of random complex networks with arbitrary clustering, extending the applicability of the configuration model and generating function formulation.