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Targeted attack on correlated interdependent networks with dependency groups

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  • Wang, Jian
  • Fang, Hongying
  • Qin, Xiaolin

Abstract

Many different cyber–physical infrastructure systems can be described as interdependent networks. Attacking important infrastructures deliberately leads to catastrophic influences on the interdependent systems. In this paper, we propose a toy model to describe the targeted attack on interdependent networks with groups. Through a percolation theory analysis, we find that attacking hubs is more likely to destroy the giant cluster. For homogeneous artificial interdependent networks, attacking nodes with any degrees cannot alter the phase transition. However, for heterogeneous correlated interdependent networks, the giant cluster decreases discontinuously (continuously) with the fraction of initial failed nodes when nodes with large (small) degree are more likely to be attacked. The theory can well predict the numerical simulation results.

Suggested Citation

  • Wang, Jian & Fang, Hongying & Qin, Xiaolin, 2019. "Targeted attack on correlated interdependent networks with dependency groups," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119311409
    DOI: 10.1016/j.physa.2019.121952
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    References listed on IDEAS

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    Cited by:

    1. Zang, Weifei & Ji, Xinsheng & Liu, Shuxin & Wang, Gengrun, 2021. "Percolation on interdependent networks with cliques and weak interdependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. Han, Jihui & Zhang, Ge & Dong, Gaogao & Zhao, Longfeng & Shi, Yuefeng & Zou, Yijiang, 2024. "Exact analysis of generalized degree-based percolation without memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    3. Zhou, Lili & Yin, Jun & Tan, Fei & Liao, Haibin, 2023. "Robustness analysis of edge-coupled interdependent networks under different attack strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).

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