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Clairvoyant targeted attack on complex networks

Author

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  • Giovanna Ferraro
  • Antonio Iovanella

Abstract

Complex networks' resilience against attacks represents a crucial issue in terms of network structure integrity. We investigate the effect of removing nodes on the network diameter in the case of a simultaneous targeted attack and sequential targeted attack. The analysis has been implemented on several network instances, taking into account different centrality measures and clustering coefficients values. Empirical networks have also been observed to compare the effects of the two removal schemes. According to classical literature, we assume that the network attacker has a wide-ranging knowledge of the system. It can be defined as clairvoyant since it knows, a priori, of all the characteristics of the problem's instances. This awareness is not always applicable when real networks are characterised by a dynamic environment. Hence, we distinguish between clairvoyant and non-clairvoyant attacks.

Suggested Citation

  • Giovanna Ferraro & Antonio Iovanella, 2018. "Clairvoyant targeted attack on complex networks," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 8(1), pages 41-62.
    • Handle: RePEc:ids:ijcome:v:8:y:2018:i:1:p:41-62
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    Citations

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    Cited by:

    1. Cerqueti, Roy & Ferraro, Giovanna & Iovanella, Antonio, 2019. "Measuring network resilience through connection patterns," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 320-329.
    2. Zhang, Chao & Xu, Xin & Dui, Hongyan, 2020. "Resilience Measure of Network Systems by Node and Edge Indicators," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Iovanella, Antonio, 2024. "Exploiting network science in business process management: A conceptual framework," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    4. Galati, Francesco & Bigliardi, Barbara & Galati, Roberta & Petroni, Giorgio, 2021. "Managing structural inter-organizational tensions in complex product systems projects: Lessons from the Metis case," Journal of Business Research, Elsevier, vol. 129(C), pages 723-735.
    5. Fabrizio Lillo & Giorgio Rizzini, 2024. "Modelling shock propagation and resilience in financial temporal networks," Papers 2407.09340, arXiv.org.

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