IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v96y2023i2d10.1140_epjb_s10051-022-00458-y.html
   My bibliography  Save this article

Vital node identification based on cycle structure in a multiplex network

Author

Listed:
  • Quan Ye

    (Lanzhou Jiaotong University)

  • Guanghui Yan

    (Lanzhou Jiaotong University)

  • Wenwen Chang

    (Lanzhou Jiaotong University)

  • Hao Luo

    (Gansu University of Traditional Chinese Medicine)

Abstract

Multiplex networks frame the heterogeneous nature of real systems, where the multiple roles of nodes, both functionally and structurally, are well represented. We identify these vital nodes in a multiplex network so that we can control a pandemic outbreak like COVID-19, eliminate damage from a network attack, maintain traffic, and so on. Vital node identification has attracted scientists in various fields for decades. In this paper, we propose a hybrid supra-cycle number and hybrid supra-cycle ratio based on the cycle structure, and present an extensive experimental analysis by comparing our indexes and several different indexes in four real multiplex networks on layer nodes and multiplex nodes. The experimental results show that these proposed indexes have good robustness, synchronization, and transmission dynamics. Finally, we provide an in-depth understanding of multiplex networks and cycle structure, and we sincerely hope more valuable academic achievements are proposed in the future. Graphic abstract

Suggested Citation

  • Quan Ye & Guanghui Yan & Wenwen Chang & Hao Luo, 2023. "Vital node identification based on cycle structure in a multiplex network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-16, February.
  • Handle: RePEc:spr:eurphb:v:96:y:2023:i:2:d:10.1140_epjb_s10051-022-00458-y
    DOI: 10.1140/epjb/s10051-022-00458-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-022-00458-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1140/epjb/s10051-022-00458-y?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. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    2. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    3. Wang, Xiao Fan & Chen, Guanrong, 2002. "Pinning control of scale-free dynamical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 521-531.
    4. Zhao, Dawei & Wang, Lianhai & Xu, Shujiang & Liu, Guangqi & Han, Xiaohui & Li, Shudong, 2017. "Vital layer nodes of multiplex networks for immunization and attack," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 169-175.
    5. Duan-Bing Chen & Hui Gao & Linyuan Lü & Tao Zhou, 2013. "Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    6. Saeed Osat & Ali Faqeeh & Filippo Radicchi, 2017. "Optimal percolation on multiplex networks," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
    7. Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    8. Manlio De Domenico & Albert Solé-Ribalta & Elisa Omodei & Sergio Gómez & Alex Arenas, 2015. "Ranking in interconnected multilayer networks reveals versatile nodes," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
    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. Tripathi, Richa & Reza, Amit, 2020. "A subset selection based approach to structural reducibility of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    2. Zhang, Jun-li & Fu, Yan-jun & Cheng, Lan & Yang, Yun-yun, 2021. "Identifying multiple influential spreaders based on maximum connected component decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    3. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    4. Filiposka, Sonja & Juiz, Carlos, 2015. "Community-based complex cloud data center," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 356-372.
    5. Jiang, Zhong-Yuan & Zeng, Yong & Liu, Zhi-Hong & Ma, Jian-Feng, 2019. "Identifying critical nodes’ group in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 121-132.
    6. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    7. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    8. 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.
    9. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2022. "Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network," International Review of Financial Analysis, Elsevier, vol. 84(C).
    10. Ai, Jun & He, Tao & Su, Zhan, 2023. "Identifying influential nodes in complex networks based on resource allocation similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    11. Yupeng Li & Zhaotong Wang & Xiaoyu Zhong & Fan Zou, 2019. "Identification of influential function modules within complex products and systems based on weighted and directed complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2375-2390, August.
    12. Luka Naglić & Lovro Šubelj, 2019. "War pact model of shrinking networks," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-14, October.
    13. Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    14. Osat, Saeed & Radicchi, Filippo, 2018. "Observability transition in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 745-761.
    15. Liu, Yang & Wei, Bo & Du, Yuxian & Xiao, Fuyuan & Deng, Yong, 2016. "Identifying influential spreaders by weight degree centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 86(C), pages 1-7.
    16. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    17. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    18. Zhe Li & Xinyu Huang, 2023. "Identifying Influential Spreaders Using Local Information," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    19. Yan, Jiaye & Zhou, Jiaying & Wu, Zhaoyan, 2019. "Structure identification of unknown complex-variable dynamical networks with complex coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 256-265.
    20. Xia, Ling-Ling & Song, Yu-Rong & Li, Chan-Chan & Jiang, Guo-Ping, 2018. "Improved targeted immunization strategies based on two rounds of selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 540-547.

    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:spr:eurphb:v:96:y:2023:i:2:d:10.1140_epjb_s10051-022-00458-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.