IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v416y2014icp354-360.html
   My bibliography  Save this article

Mathematic model of node matching based on adjacency matrix and evolutionary solutions

Author

Listed:
  • Yao, Xiangjuan
  • Gong, Dunwei
  • Gu, Yali

Abstract

Research on complex networks is becoming a very hot topic in recent years, among which node matching problem is an important issue. The aim of node matching problem is to find out the corresponding relations between the individuals of associated networks. Traditional node matching problem of networks always hypothesize that a proportion of matching nodes are known. However, if the ratio of matched nodes is very small, the matching accuracy of the remaining nodes cannot be evaluated accurately. What is more, we may have not any matched nodes for reference at all. In view of this, this paper established the mathematic model of node matching problem based on the adjacency matrixes of networks, and presented an evolutionary algorithm to solve it. The experimental results show that the proposed method can achieve satisfactory matching precision in the absence of any matched nodes.

Suggested Citation

  • Yao, Xiangjuan & Gong, Dunwei & Gu, Yali, 2014. "Mathematic model of node matching based on adjacency matrix and evolutionary solutions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 354-360.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:354-360
    DOI: 10.1016/j.physa.2014.08.070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114007559
    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.2014.08.070?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. Yao, Xiangjuan & Gong, Dunwei & Wang, Peipei & Chen, Lina, 2017. "Multi-objective optimization model and evolutional solution of network node matching problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 495-502.

    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:416:y:2014:i:c:p:354-360. 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.