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Complex networks under dynamic repair model

Author

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  • Chaoqi, Fu
  • Ying, Wang
  • Kun, Zhao
  • Yangjun, Gao

Abstract

Invulnerability is not the only factor of importance when considering complex networks’ security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

Suggested Citation

  • Chaoqi, Fu & Ying, Wang & Kun, Zhao & Yangjun, Gao, 2018. "Complex networks under dynamic repair model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 323-330.
  • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:323-330
    DOI: 10.1016/j.physa.2017.08.071
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    References listed on IDEAS

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    1. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    2. Wang, Jianwei & Rong, Lili & Zhang, Liang & Zhang, Zhongzhi, 2008. "Attack vulnerability of scale-free networks due to cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6671-6678.
    3. Weiman Sun & An Zeng, 2017. "Target recovery in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(1), pages 1-6, January.
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    Cited by:

    1. Nguyen, Win P.V. & Nof, Shimon Y., 2020. "Strategic lines of collaboration in response to disruption propagation (CRDP) through cyber-physical systems," International Journal of Production Economics, Elsevier, vol. 230(C).

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