IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0192874.html
   My bibliography  Save this article

Optimization of robustness of interdependent network controllability by redundant design

Author

Listed:
  • Zenghu Zhang
  • Yongfeng Yin
  • Xin Zhang
  • Lijun Liu

Abstract

Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability.

Suggested Citation

  • Zenghu Zhang & Yongfeng Yin & Xin Zhang & Lijun Liu, 2018. "Optimization of robustness of interdependent network controllability by redundant design," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-17, February.
  • Handle: RePEc:plo:pone00:0192874
    DOI: 10.1371/journal.pone.0192874
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0192874
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0192874&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0192874?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Guizhen & Qi, Xiaogang & Liu, Lifang, 2020. "Research on network robustness based on different deliberate attack methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    2. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    3. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    4. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    5. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    6. Bo Zhang & Jianping Yuan & J. F. Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Global Feedback Control for Coordinated Linear Switched Reluctance Machines Network with Full-State Observation and Internal Model Compensation," Energies, MDPI, vol. 10(12), pages 1-19, December.
    7. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    8. Yan Zhang & Antonios Garas & Frank Schweitzer, 2019. "Control Contribution Identifies Top Driver Nodes In Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, December.
    9. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    10. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    11. Zhang, Rui & Wang, Xiaomeng & Cheng, Ming & Jia, Tao, 2019. "The evolution of network controllability in growing networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 257-266.
    12. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    13. Neil Johnson & Guannan Zhao & Eric Hunsader & Jing Meng & Amith Ravindar & Spencer Carran & Brian Tivnan, 2012. "Financial black swans driven by ultrafast machine ecology," Papers 1202.1448, arXiv.org.
    14. Chen, Shi-Ming & Xu, Yun-Fei & Nie, Sen, 2017. "Robustness of network controllability in cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 536-539.
    15. Xizhe Zhang & Huaizhen Wang & Tianyang Lv, 2017. "Efficient target control of complex networks based on preferential matching," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-10, April.
    16. Badhwar, Rahul & Bagler, Ganesh, 2017. "A distance constrained synaptic plasticity model of C. elegans neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 313-322.
    17. Lazaro M Sanchez-Rodriguez & Yasser Iturria-Medina & Erica A Baines & Sabela C Mallo & Mehdy Dousty & Roberto C Sotero & on behalf of The Alzheimer’s Disease Neuroimaging Initiative, 2018. "Design of optimal nonlinear network controllers for Alzheimer's disease," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-24, May.
    18. Pang, Shao-Peng & Hao, Fei, 2018. "Effect of interaction strength on robustness of controlling edge dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 246-257.
    19. Christos Ellinas & Neil Allan & Anders Johansson, 2016. "Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective," Systems Engineering, John Wiley & Sons, vol. 19(3), pages 179-192, May.
    20. Hiroyasu Inoue, 2015. "Analyses of Aggregate Fluctuations of Firm Network Based on the Self-Organized Criticality Model," Papers 1512.05066, arXiv.org, revised Apr 2016.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0192874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.