IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v413y2014icp515-522.html
   My bibliography  Save this article

Predicting missing links via effective paths

Author

Listed:
  • Zhu, Xuzhen
  • Tian, Hui
  • Cai, Shimin

Abstract

Recently, in complex network, link prediction has brought a surge of researches, among which similarity based link prediction outstandingly gains considerable success, especially similarity in terms of paths. In investigation of paths based similarity, we find that the effective influence of endpoints and strong connectivity make paths contribute more similarity between two unconnected endpoints, leading to a more accurate link prediction. Accordingly, we propose a so-called effective path index (EP) in this paper to leverage effective influence of endpoints and strong connectivity in similarity calculation. For demonstrating excellence of our index, the comparisons with six mainstream indices are performed on experiments in 15 real datasets and results show a great improvement of performance via our index.

Suggested Citation

  • Zhu, Xuzhen & Tian, Hui & Cai, Shimin, 2014. "Predicting missing links via effective paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 515-522.
    • Handle: RePEc:eee:phsmap:v:413:y:2014:i:c:p:515-522
    DOI: 10.1016/j.physa.2014.07.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114005986
    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.2014.07.029?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. Lawford, Steve & Mehmeti, Yll, 2020. "Cliques and a new measure of clustering: With application to U.S. domestic airlines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    2. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Enhanced collective influence: A paradigm to optimize network disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 43-52.
    3. Liu, Shuxin & Ji, Xinsheng & Liu, Caixia & Bai, Yi, 2017. "Extended resource allocation index for link prediction of complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 174-183.
    4. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Predicting the evolution of complex networks via similarity dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 662-672.
    5. Kumar, Ajay & Mishra, Shivansh & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction in complex networks based on Significance of Higher-Order Path Index (SHOPI)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    6. Yao, Yabing & Zhang, Ruisheng & Yang, Fan & Tang, Jianxin & Yuan, Yongna & Hu, Rongjing, 2018. "Link prediction in complex networks based on the interactions among paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 52-67.

    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:413:y:2014:i:c:p:515-522. 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.