A unified model for Sierpinski networks with scale-free scaling and small-world effect

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dc.creatorGuan, Jihong
dc.creatorWu, Yuewen
dc.creatorZhang, Zhongzhi
dc.creatorZhou, Shuigeng
dc.creatorWu, Yonghui
dc.date2009-03-24
dc.date.accessioned2026-07-07T13:01:35Z
dc.date.available2026-07-07T13:01:35Z
dc.descriptionIn this paper, we propose an evolving Sierpinski gasket, based on which we establish a model of evolutionary Sierpinski networks (ESNs) that unifies deterministic Sierpinski network [Eur. Phys. J. B {\bf 60}, 259 (2007)] and random Sierpinski network [Eur. Phys. J. B {\bf 65}, 141 (2008)] to the same framework. We suggest an iterative algorithm generating the ESNs. On the basis of the algorithm, some relevant properties of presented networks are calculated or predicted analytically. Analytical solution shows that the networks under consideration follow a power-law degree distribution, with the distribution exponent continuously tuned in a wide range. The obtained accurate expression of clustering coefficient, together with the prediction of average path length reveals that the ESNs possess small-world effect. All our theoretical results are successfully contrasted by numerical simulations. Moreover, the evolutionary prisoner's dilemma game is also studied on some limitations of the ESNs, i.e., deterministic Sierpinski network and random Sierpinski network.
dc.descriptionfinal version accepted for publication in Physica A
dc.identifierhttps://arxiv.org/abs/0903.3997
dc.identifierhttp://arxiv.org/abs/0903.3997
dc.identifierPhysica A, 2009, 388: 2571-2578.
dc.identifierdoi:10.1016/j.physa.2009.03.005
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/226197
dc.subjectDisordered Systems and Neural Networks
dc.subjectPhysics and Society
dc.titleA unified model for Sierpinski networks with scale-free scaling and small-world effect
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