SPR-based Markov chain method for degree distributions of evolving networks

SPR-based Markov chain method for degree distributions of evolving networks
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基于SPR的马尔可夫链方法求解演化网络的度分布

DOI:
10.1016/j.physa.2012.01.040
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发表时间:
2012-06
期刊:
Physica A: Statistical Mechanics and Its Applications
影响因子:
--
通讯作者:
Rayman-Bacchus, Lez
Rayman-Bacchus, Lez
中科院分区:
其他
文献类型:
--
作者:
Zhang, Xiaojun;He, Zishu;He, Zheng;Rayman-Bacchus, Lez

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本文提出了一种基于随机过程规则(SPR)的马尔可夫链方法来计算演化网络的度分布。这种新方法克服了Shih,Chen和Liu使用马尔可夫链方法的两个缺点(Shih et al.2005年,[21])。此外,我们还展示了如何有效地使用基于SPR的马尔可夫链方法来计算随机生灭网络的度分布,这是我们认为是新颖的。首先引入SPR来代替传统的进化规则,使得在一个样本空间中计算度分布成为可能。然后介绍了基于SPR的马尔可夫链方法,并用它计算了两种进化网络。最后,也是最重要的,将基于SPR的方法应用于随机生灭网络的度分布计算问题。
In this paper, we develop a stochastic process rules (SPR) based Markov chain method to calculate the degree distributions of evolving networks. This new approach overcomes two shortcomings of Shi, Chen and Liu’s use of the Markov chain method (Shi et al. 2005 [21]). In addition we show how an SPR-based Markov chain method can be effectively used to calculate degree distributions of random birth-and-death networks, which we believe to be novel. First SPR are introduced to replace traditional evolving rules (TR), making it possible to compute degree distributions in one sample space. Then the SPR-based Markov chain method is introduced and tested by using it to calculate two kinds of evolving network. Finally and most importantly, the SPR-based method is applied to the problem of calculating the degree distributions of random birth-and-death networks.
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影响因子: --
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