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arXiv 0811.4511cond-mat.stat-mechcond-mat.dis-nn
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成簇网络上键逾渗和k核尺寸的分析结果

Analytical results for bond percolation and k-core sizes on clustered networks

James P. Gleeson, Sergey Melnik

摘要:

提出了一种计算非零聚集结构随机网络的键逾渗阈值、$k$ -核尺寸和巨连通元尺寸的解析方法。网络是使用Trapman的[P. Trapman,Theor. Pop. Biol. { \bf 71},160(2007)]嵌入树状随机图中的团的模型。由此产生的网络具有任意的度分布和可调的度相关聚类。研究了聚集对这种类型网络的键逾渗阈值的影响,并与文献中的一些最新结果进行了对比。对于非常高的聚类水平,这些广义Trapman网络中的逾渗阈值增加到高于具有相同度分布的随机连接(未聚类)网络中的值。在无标度网络中,度分布的方差是无穷大的,这种聚集效应可能导致非零的渗流(流行病)阈值。

英文摘要:

An analytical approach to calculating bond percolation thresholds, sizes of $k$-cores, and sizes of giant connected components on structured random networks with non-zero clustering is presented. The networks are generated using a generalization of Trapman's [P. Trapman, Theor. Pop. Biol. {\bf 71}, 160 (2007)] model of cliques embedded in tree-like random graphs. The resulting networks have arbitrary degree distributions and tunable degree-dependent clustering. The effect of clustering on the bond percolation thresholds for networks of this type is examined and contrasted with some recent results in the literature. For very high levels of clustering the percolation threshold in these generalized Trapman networks is increased above the value it takes in a randomly-wired (unclustered) network of the same degree distribution. In assortative scale-free networks, where the variance of the degree distribution is infinite, this clustering effect can lead to a non-zero percolation (epidemic) threshold.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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