IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0112039.html
   My bibliography  Save this article

Structural Controllability of Complex Networks Based on Preferential Matching

Author

Listed:
  • Xizhe Zhang
  • Tianyang Lv
  • XueYing Yang
  • Bin Zhang

Abstract

Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small number of MDSs, because enumerating all of the MDSs of a network is a #P problem. Therefore, past research has not been sufficient to arrive at a convincing conclusion. In this paper, first, we propose a preferential matching algorithm to find MDSs that have a specific degree property. Then, we show that the MDSs obtained by preferential matching can be composed of high- and medium-degree nodes. Moreover, the experimental results also show that the average degree of the MDSs of some networks tends to be greater than that of the overall network, even when the MDSs are obtained using previous research method. Further analysis shows that whether the driver nodes tend to be high-degree nodes or not is closely related to the edge direction of the network.

Suggested Citation

  • Xizhe Zhang & Tianyang Lv & XueYing Yang & Bin Zhang, 2014. "Structural Controllability of Complex Networks Based on Preferential Matching," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-8, November.
  • Handle: RePEc:plo:pone00:0112039
    DOI: 10.1371/journal.pone.0112039
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112039
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0112039&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0112039?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. Fragkiskos Papadopoulos & Maksim Kitsak & M. Ángeles Serrano & Marián Boguñá & Dmitri Krioukov, 2012. "Popularity versus similarity in growing networks," Nature, Nature, vol. 489(7417), pages 537-540, September.
    2. Gourab Ghoshal & Albert-László Barabási, 2011. "Ranking stability and super-stable nodes in complex networks," Nature Communications, Nature, vol. 2(1), pages 1-7, September.
    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. Zhang, Xizhe & Zhu, Yuyan & Zhao, Yongkang, 2021. "Altering control modes of complex networks by reversing edges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(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. Jascha-Alexander Koch & Michael Siering, 2019. "The recipe of successful crowdfunding campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 661-679, December.
    2. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    3. Valeria Costantini & Valerio Leone Sciabolazza & Elena Paglialunga, 2023. "Network-driven positive externalities in clean energy technology production: the case of energy efficiency in the EU residential sector," The Journal of Technology Transfer, Springer, vol. 48(2), pages 716-748, April.
    4. Haochuan Cui & Tiewei Li & Cheng-Jun Wang, 2023. "Climbing up the ladder of abstraction: how to span the boundaries of knowledge space in the online knowledge market?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    5. Wang, Mingyan & Zeng, An & Cui, Xiaohua, 2022. "Collective user switching behavior reveals the influence of TV channels and their hidden community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    6. Martin Keller-Ressel & Stephanie Nargang, 2020. "The hyperbolic geometry of financial networks," Papers 2005.00399, arXiv.org, revised May 2020.
    7. Weihua Yang & David Rideout, 2020. "High Dimensional Hyperbolic Geometry of Complex Networks," Mathematics, MDPI, vol. 8(11), pages 1-39, October.
    8. Wang, Zuxi & Li, Qingguang & Jin, Fengdong & Xiong, Wei & Wu, Yao, 2016. "Hyperbolic mapping of complex networks based on community information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 104-119.
    9. Gerardo Iñiguez & Carlos Pineda & Carlos Gershenson & Albert-László Barabási, 2022. "Dynamics of ranking," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    10. Shang, Ke-ke & Small, Michael & Yan, Wei-sheng, 2017. "Fitness networks for real world systems via modified preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 49-60.
    11. Maksim Kitsak & Alexander Ganin & Ahmed Elmokashfi & Hongzhu Cui & Daniel A. Eisenberg & David L. Alderson & Dmitry Korkin & Igor Linkov, 2023. "Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    12. Zhenpeng Li & Luo Li, 2023. "The Generation Mechanism of Degree Distribution with Power Exponent >2 and the Growth of Edges in Temporal Social Networks," Mathematics, MDPI, vol. 11(13), pages 1-11, June.
    13. Wu, Zhenyu & Zou, Ming, 2014. "Modeling social tagging using latent interaction potential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 125-133.
    14. Jhun, Bukyoung & Jo, Minjae & Kahng, B., 2022. "Quantum contact process on scale-free networks," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    15. Hou, Lei & Liu, Kecheng & Liu, Jianguo & Zhang, Runtong, 2017. "Solving the stability–accuracy–diversity dilemma of recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 415-424.
    16. Shang, Yilun, 2016. "On the likelihood of forests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 157-166.
    17. Wang, Luo-Qing & Xu, Yong-Xiang, 2018. "Assessing the relevance of individual characteristics for the structure of similarity networks in new social strata in Shanghai," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 881-889.
    18. Xu-Cheng Yin & Bo-Wen Zhang & Xiao-Ping Cui & Jiao Qu & Bin Geng & Fang Zhou & Li Song & Hong-Wei Hao, 2016. "ISART: A Generic Framework for Searching Books with Social Information," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-27, February.
    19. Tsouri, Maria & Hansen, Teis & Hanson, Jens & Steen, Markus, 2022. "Knowledge recombination for emerging technological innovations: The case of green shipping," Technovation, Elsevier, vol. 114(C).
    20. Pu, Jia & Jia, Tao & Li, Ya, 2019. "Effects of time cost on the evolution of cooperation in snowdrift game," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 146-151.

    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:pone00:0112039. 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.