IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v2y2011i1d10.1038_ncomms1396.html
   My bibliography  Save this article

Ranking stability and super-stable nodes in complex networks

Author

Listed:
  • Gourab Ghoshal

    (Biology and Computer Science, Center for Complex Network Research, Northeastern University
    Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute
    Media Laboratory, Massachusetts Institute of Technology)

  • Albert-László Barabási

    (Biology and Computer Science, Center for Complex Network Research, Northeastern University
    Harvard Medical School, and Center for Cancer Systems Biology, Dana-Farber Cancer Institute)

Abstract

Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:2:y:2011:i:1:d:10.1038_ncomms1396
    DOI: 10.1038/ncomms1396
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms1396
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/ncomms1396?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
    ---><---

    Citations

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


    Cited by:

    1. 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.
    2. 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.
    3. Gerardo Iñiguez & Carlos Pineda & Carlos Gershenson & Albert-László Barabási, 2022. "Dynamics of ranking," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. 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.
    5. Zhang, Jianhua & Zhou, Yu & Wang, Shuliang & Min, Qinjie, 2024. "Critical station identification and robustness analysis of urban rail transit networks based on comprehensive vote-rank algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    6. 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).
    7. Eleanor R Brush & David C Krakauer & Jessica C Flack, 2013. "A Family of Algorithms for Computing Consensus about Node State from Network Data," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-17, July.

    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:nat:natcom:v:2:y:2011:i:1:d:10.1038_ncomms1396. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.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.