Deterministically delayed pseudofractal networks
Deterministically delayed pseudofractal networks
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DOI：
10.1088/1742-5468/2011/10/p10032
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Wei-gang Sun;Yongqing Wu;Guanrong Chen;Qingyun Wang
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文献类型：
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作者：
Wei-gang Sun;Yongqing Wu;Guanrong Chen;Qingyun Wang
在伪分形网络（PFNs）的基础上，提出了一类延迟伪分形网络（DPFNs），它的一个特点是新加入的边延迟产生新的节点，不同于PFNs的所有边同时产生新节点的演化算法.得到了度分布、聚类系数C和平均路径长度APL的解析表达式。我们比较了DPFNs和PFNs，发现DPFNs的度分布指数小于PFNs，这意味着这种延迟网络的异质性更高。与PFN相比，DPFN的小世界特征更加突出（C更大，APL更小）。我们还发现，延迟加强无标度和小世界特性的DPFNs。此外，我们计算和比较的平均首次通过时间（MFPT）的数值，显示DPFNs的MFPT较短。我们的研究可能有助于更深入地了解各种确定性增长的延迟网络。
On the basis of pseudofractal networks (PFNs), we propose a family of delayed pseudofractal networks (DPFNs) with a special feature that newly added edges delay producing new nodes, differing from the evolution algorithms of PFNs where all existing edges simultaneously generate new nodes. We obtain analytical formulae for degree distribution, clustering coefficient (C) and average path length (APL). We compare DPFNs and PFNs, and show that the exponent of the degree distribution of DPFNs is smaller than that of PFNs, meaning that the heterogeneity of this kind of delayed network is higher. Compared to PFNs, small-world features of DPFNs are more prominent (larger C and smaller APL). We also find that the delay strengthens the scale-free and small-world characteristics of DPFNs. In addition, we calculate and compare the mean first passage time (MFPT) numerically, revealing that the MFPT of DPFNs is shorter. Our study may help with a deeper understanding of various deterministically growing delayed networks.