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Target recovery in complex networks

Author

Listed:
  • Weiman Sun

    (Beijing Normal University)

  • An Zeng

    (School of Systems Science, Beijing Normal University)

Abstract

The invulnerability of complex networks is an important issue which has been widely analyzed in different fields. A lot of works have been done to measure and improve the stability of complex networks when being attacked. Recently, how to recover networks after attack was intensively studied. The existing methods are mainly designed to recover the overall functionality of networks, yet in many real cases the recovery of important nodes should be given priority, to which we refer target recovery. For example, when the cold wave paralyses the railway networks, target recovery means to repair those stations or railways such that the transport capacity of densely-populated cities can be recovered as fast as possible. In this paper, we first compare the impact of attacks on the whole network and target nodes respectively, and then study the efficiency of traditional recovery methods that are proposed based on global centrality metrics. Furthermore, based on target centrality metrics, we introduce a local betweenness recovery method and we find it has better performance than the traditional methods. We finally propose a hybrid recovery method which includes local betweenness metric and local closeness metric. The performance of the hybrid method is shown to be similar to that of the greedy algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:eurphb:v:90:y:2017:i:1:d:10.1140_epjb_e2016-70618-0
    DOI: 10.1140/epjb/e2016-70618-0
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    Citations

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    Cited by:

    1. Zhang, Min & Wang, Jinman & Feng, Yu, 2019. "Temporal and spatial change of land use in a large-scale opencast coal mine area: A complex network approach," Land Use Policy, Elsevier, vol. 86(C), pages 375-386.
    2. Kong, Jingjing & Zhang, Chao & Simonovic, Slobodan P., 2021. "Optimizing the resilience of interdependent infrastructures to regional natural hazards with combined improvement measures," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Rachunok, Benjamin & Nateghi, Roshanak, 2020. "The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Zhang, Yuhui & Wen, Wen & Wang, Liwei, 2024. "Resilience assessment of airport aircraft area network operations under thunderstorm weather," Journal of Air Transport Management, Elsevier, vol. 119(C).
    5. Fu, Chaoqi & Wang, Ying & Gao, Yangjun & Wang, Xiaoyang, 2017. "Complex networks repair strategies: Dynamic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 401-406.
    6. 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.

    More about this item

    Keywords

    Statistical and Nonlinear Physics;

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