IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v520y2019icp257-266.html
   My bibliography  Save this article

The evolution of network controllability in growing networks

Author

Listed:
  • Zhang, Rui
  • Wang, Xiaomeng
  • Cheng, Ming
  • Jia, Tao

Abstract

The study of network structural controllability focuses on the minimum number of driver nodes needed to control a whole network. Despite intensive studies on this topic, most of them consider static networks only. It is well-known, however, that real networks are growing, with new nodes and links added to the system. Here, we analyze controllability of evolving networks and propose a general rule for the change of driver nodes. We further apply the rule to solve the problem of network augmentation subject to the controllability constraint. The findings strengthen our understandings of network controllability and shed light on controllability of real systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:520:y:2019:i:c:p:257-266
    DOI: 10.1016/j.physa.2019.01.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119300433
    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.2019.01.042?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. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Berryhill, Benjamin & Yazdani, Alireza, 2016. "Characterizing the topological and controllability features of U.S. power transmission networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 84-98.
    2. Ravindran, Vandana & V., Sunitha & Bagler, Ganesh, 2017. "Identification of critical regulatory genes in cancer signaling network using controllability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 134-143.
    3. Wang, Pei & Xu, Shuang, 2017. "Spectral coarse grained controllability of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 168-176.
    4. Jalili, Mahdi, 2018. "Effective augmentation of networked systems and enhancing pinning controllability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 155-161.
    5. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    6. 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.
    7. 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.
    8. 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.
    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. Yu, Xiaoyao & Liang, Yongqing & Wang, Xiaomeng & Jia, Tao, 2021. "The network asymmetry caused by the degree correlation and its effect on the bimodality in control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(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. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    2. Liu, Suling & Xu, Qiong & Chen, Aimin & Wang, Pei, 2020. "Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. 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.
    4. 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.
    5. 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.
    6. Zhang, Xizhe & Li, Qian, 2019. "Altering control modes of complex networks based on edge removal," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 185-193.
    7. Yang, Qing-Lin & Wang, Li-Fu & Zhao, Guo-Tao & Guo, Ge, 2020. "A coarse graining algorithm based on m-order degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    8. Guo, Tianjiao & Tu, Lilan & Guo, Yifei & Hu, Jia & Su, Qingqing, 2023. "Control-capacity analysis and optimized construction for controlled interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    9. 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.
    10. Liu, Jie & Schonfeld, Paul M. & Shuai, Chunyan & He, Mingwei & Wang, Kelvin C.P., 2022. "The controllability of China’s high-speed rail network in terms of delivering emergency supplies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Wu, Jason & Baker, Jack W., 2020. "Statistical learning techniques for the estimation of lifeline network performance and retrofit selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    16. 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).
    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. 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.

    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:520:y:2019:i:c:p:257-266. 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.