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Efficient calculation of the robustness measure R for complex networks

Author

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  • Hong, Chen
  • He, Ning
  • Lordan, Oriol
  • Liang, Bo-Yuan
  • Yin, Nai-Yu

Abstract

In a recent work, Schneider et al. (2011) proposed a new measure R for network robustness, where the value of R is calculated within the entire process of malicious node attacks. In this paper, we present an approach to improve the calculation efficiency of R, in which a computationally efficient robustness measure R′ is introduced when the fraction of failed nodes reaches to a critical threshold qc. Simulation results on three different types of network models and three real networks show that these networks all exhibit a computationally efficient robustness measure R′. The relationships between R′ and the network size N and the network average degree 〈k〉 are also explored. It is found that the value of R′ decreases with N while increases with 〈k〉. Our results would be useful for improving the calculation efficiency of network robustness measure R for complex networks.

Suggested Citation

  • Hong, Chen & He, Ning & Lordan, Oriol & Liang, Bo-Yuan & Yin, Nai-Yu, 2017. "Efficient calculation of the robustness measure R for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 63-68.
    • Handle: RePEc:eee:phsmap:v:478:y:2017:i:c:p:63-68
    DOI: 10.1016/j.physa.2017.02.054
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    Cited by:

    1. Sohn, Insoo, 2019. "A robust complex network generation method based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 593-601.

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