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聚类如何影响复杂网络中的键渗流阈值。

How clustering affects the bond percolation threshold in complex networks.

作者信息

Gleeson James P, Melnik Sergey, Hackett Adam

机构信息

Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Jun;81(6 Pt 2):066114. doi: 10.1103/PhysRevE.81.066114. Epub 2010 Jun 18.

Abstract

The question of how clustering (nonzero density of triangles) in networks affects their bond percolation threshold has important applications in a variety of disciplines. Recent advances in modeling highly clustered networks are employed here to analytically study the bond percolation threshold. In comparison to the threshold in an unclustered network with the same degree distribution and correlation structure, the presence of triangles in these model networks is shown to lead to a larger bond percolation threshold (i.e. clustering increases the epidemic threshold or decreases resilience of the network to random edge deletion).

摘要

网络中的聚类(三角形的非零密度)如何影响其键渗流阈值这一问题在各种学科中都有重要应用。本文利用高度聚类网络建模的最新进展来分析研究键渗流阈值。与具有相同度分布和相关结构的非聚类网络中的阈值相比,这些模型网络中三角形的存在会导致更大的键渗流阈值(即聚类增加了流行阈值或降低了网络对随机边删除的恢复力)。

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