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Resistance maximization principle for defending networks against virus attack

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  • Li, Angsheng
  • Zhang, Xiaohui
  • Pan, Yicheng

Abstract

We investigate the defending of networks against virus attack. We define the resistance of a network to be the maximum number of bits required to determine the code of the module that is accessible from random walk, from which random walk cannot escape. We show that for any network G, R(G)=H1(G)−H2(G), where R(G) is the resistance of G, H1(G) and H2(G) are the one- and two-dimensional structural information of G, respectively, and that resistance maximization is the principle for defending networks against virus attack. By using the theory, we investigate the defending of real world networks and of the networks generated by the preferential attachment and the security models. We show that there exist networks that are defensible by a small number of controllers from cascading failure of any virus attack. Our theory demonstrates that resistance maximization is the principle for defending networks against virus attacks.

Suggested Citation

  • Li, Angsheng & Zhang, Xiaohui & Pan, Yicheng, 2017. "Resistance maximization principle for defending networks against virus attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 211-223.
    • Handle: RePEc:eee:phsmap:v:466:y:2017:i:c:p:211-223
    DOI: 10.1016/j.physa.2016.09.009
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    References listed on IDEAS

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    1. Jianxi Gao & Baruch Barzel & Albert-László Barabási, 2016. "Universal resilience patterns in complex networks," Nature, Nature, vol. 530(7590), pages 307-312, February.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    3. Li, Angsheng & Li, Jiankou & Pan, Yicheng, 2015. "Discovering natural communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 878-896.
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    Cited by:

    1. Yin, Likang & Deng, Yong, 2018. "Toward uncertainty of weighted networks: An entropy-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 176-186.

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