Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2009-03-24
Physica A, 2009, 388: 2571-2578.
Physics
Condensed Matter
Disordered Systems and Neural Networks
final version accepted for publication in Physica A
Scientific paper
10.1016/j.physa.2009.03.005
In 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.
Guan Jihong
Wu Yonghui
Wu Yuewen
Zhang Zhongzhi
Zhou Shuigeng
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