Survivability analysis of weighted-edge attacks on complex networks with incomplete information

被引:14
|
作者
Yin, Yong [1 ]
Liu, Qiong [2 ]
Zhang, Chaoyong [2 ]
Zhou, Jian [1 ]
机构
[1] Wuhan Univ Technol, Key Lab Fiber Opt Sensing Technol & Informat Proc, Minist Educ, Wuhan 430070, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Complex network; Survivability; Weighted edge; Incomplete information; GROWTH;
D O I
10.1016/j.physa.2019.04.193
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
We study the survivability of weighted-edge attacks with incomplete information on a free-scale network. We consider a situation where attackers can detect partial edges of the network, and the information regarding the detected edges may be imprecise. Random and intentional attacks are the two extreme cases of this investigation. In this article, edge betweenness is adopted to describe the weight of an edge. Based on this, alpha is employed as the parameter describing the scope of edges that can be detected from the network and beta as the parameter depicting the accuracy of the already detected edge weight information. Attack strategies that are different from both random and targeted attacks are designed for a scale-free network with incomplete information. Numerical simulations are performed and results are obtained: (i) a larger alpha or beta can worsen both the network connectivity and efficiency while confronting attacks; (ii) when a is small, beta has a relatively small impact on the network connectivity sigma, but with the increase in a, both alpha and beta both play important roles in it; (iii) beta consistently plays an important role in network efficiency, regardless of the value of alpha. The results of this article are helpful for the future development of effective protection strategies in scale-free networks as it is more convenient and realistic to protect network information than to adjust the network structural topology. (C) 2019 Elsevier B.V. All rights reserved.
引用
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页数:8
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