How clustering affects the bond percolation threshold in complex networks

被引:69
作者
Gleeson, James P. [1 ]
Melnik, Sergey [1 ]
Hackett, Adam [1 ]
机构
[1] Univ Limerick, Dept Math & Stat, Limerick, Ireland
基金
爱尔兰科学基金会;
关键词
RANDOM GRAPHS; EPIDEMICS; DISEASE; SPREAD;
D O I
10.1103/PhysRevE.81.066114
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
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)
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页数:10
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