IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v512y2018icp14-26.html
   My bibliography  Save this article

Target control of edge dynamics in complex networks

Author

Listed:
  • Pang, Shao-Peng
  • Hao, Fei

Abstract

Controlling edge dynamics in complex networks, which is relevant to many real systems, is significant in network science. However, the full control of the edge dynamics may be neither feasible nor necessary in some real systems with huge size and complexity. This practical situation motivates us to explore target control, that is, the full control of a preselected subset of edges. In this paper, an effective method is proposed to approximate the minimum number of driver nodes sufficient for target control of the edge dynamics in complex networks. The method allows us not only to analyze the target control efficiency, i.e., a unified metric of the propensity of an edge dynamics to be target controllable, but also to show the structural property of driver nodes. Evaluation of real networks indicates that the target control efficiency is determined mainly by the network’s degree distribution. Simulation results and analytic calculations show that the edge dynamics in dense and homogeneous networks tend to have higher target control efficiency. Also, the target edge set selected by the random scheme is easier to control than that by the local scheme and the critical scheme. Furthermore, the analysis of the local structure of driver nodes shows that spare and inhomogeneous networks, which emerge in many real systems, tend to choose high-degree and divergent nodes as driver nodes.

Suggested Citation

  • Pang, Shao-Peng & Hao, Fei, 2018. "Target control of edge dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 14-26.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:14-26
    DOI: 10.1016/j.physa.2018.08.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711830952X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.08.011?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Francesco Lo Iudice & Franco Garofalo & Francesco Sorrentino, 2015. "Structural permeability of complex networks to control signals," Nature Communications, Nature, vol. 6(1), pages 1-6, December.
    2. M. Catanzaro & R. Pastor-Satorras, 2005. "Analytic solution of a static scale-free network model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 44(2), pages 241-248, March.
    3. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    4. Le-Zhi Wang & Ri-Qi Su & Zi-Gang Huang & Xiao Wang & Wen-Xu Wang & Celso Grebogi & Ying-Cheng Lai, 2016. "A geometrical approach to control and controllability of nonlinear dynamical networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Alexander Tselykh & Vladislav Vasilev & Larisa Tselykh & Fernando A. F. Ferreira, 2022. "Influence control method on directed weighted signed graphs with deterministic causality," Annals of Operations Research, Springer, vol. 311(2), pages 1281-1305, April.
    4. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    5. Pang, Shaopeng & Hao, Fei, 2017. "Optimizing controllability of edge dynamics in complex networks by perturbing network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 217-227.
    6. Priyan Bhattacharya & Karthik Raman & Arun K Tangirala, 2022. "Discovering adaptation-capable biological network structures using control-theoretic approaches," PLOS Computational Biology, Public Library of Science, vol. 18(1), pages 1-28, January.
    7. Liu, Yangyang & Zhao, Chengli & Zhang, Xue & Yi, Dongyun & Chen, Wen, 2018. "Core structure: The coupling failure procedure in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 1-11.
    8. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:eee:phsmap:v:512:y:2018:i:c:p:14-26. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.