IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v429y2015icp169-176.html
   My bibliography  Save this article

Identifying node importance in complex networks

Author

Listed:
  • Hu, Ping
  • Fan, Wenli
  • Mei, Shengwei

Abstract

In this paper, we propose a novel node importance evaluation method from the perspective of the existence of mutual dependence among nodes. The node importance comprises its initial importance and the importance contributions from both the adjacent and non-adjacent nodes according to the dependence strength between them. From the simulation analyses on an example network and the ARPA network, we observe that our method can well identify the node importance. Then, the cascading failures on the Netscience and E-mail networks demonstrate that the networks are more vulnerable when continuously removing the important nodes identified by our method, which further proves the accuracy of our method.

Suggested Citation

  • Hu, Ping & Fan, Wenli & Mei, Shengwei, 2015. "Identifying node importance in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 169-176.
    • Handle: RePEc:eee:phsmap:v:429:y:2015:i:c:p:169-176
    DOI: 10.1016/j.physa.2015.02.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115000965
    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.2015.02.002?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.

    Citations

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


    Cited by:

    1. Dong, Lijun & Wang, Yi & Liu, Ran & Pi, Benjie & Wu, Liuyi, 2016. "Toward edge minability for role mining in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 274-286.
    2. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    3. Hu, Ping & Fan, Wen-Li, 2020. "Mitigation strategy against cascading failures considering vulnerable transmission line in power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    4. Hu, Ping & Mei, Ting, 2018. "Ranking influential nodes in complex networks with structural holes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 624-631.
    5. Zhu, Canshi & Wang, Xiaoyang & Zhu, Lin, 2017. "A novel method of evaluating key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 43-50.
    6. Wang, Ning & Gao, Ying & He, Jia-tao & Yang, Jun, 2022. "Robustness evaluation of the air cargo network considering node importance and attack cost," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Liu, Wei & Song, Zhaoyang, 2020. "Review of studies on the resilience of urban critical infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    8. Huang, Wencheng & Li, Haoran & Yin, Yanhui & Zhang, Zhi & Xie, Anhao & Zhang, Yin & Cheng, Guo, 2024. "Node importance identification of unweighted urban rail transit network: An Adjacency Information Entropy based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Zhou, Jin & Xu, Weixiang & Guo, Xin & Liu, Xumin, 2017. "A hierarchical network modeling method for railway tunnels safety assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 226-239.
    10. Col, Alcebiades Dal & Petronetto, Fabiano, 2023. "Graph regularization centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

    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:429:y:2015:i:c:p:169-176. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.