IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v428y2015icp111-132.html
   My bibliography  Save this article

Growing network: Models following nonlinear preferential attachment rule

Author

Listed:
  • Zadorozhnyi, V.N.
  • Yudin, E.B.

Abstract

We investigate the preferential attachment graphs proceeding from the following two assumptions. The first one: the probability that a new vertex connects to a vertex i is proportional to an arbitrary nonnegative function f of a vertex degree k. The second assumption: a new vertex can have a random number of edges. We derive formulas for any f to determine the vertex degree distribution {Qk} in generated graphs. The inverse problem is solved: we have obtained formulas, that allow from a given distribution {Qk} to determine f (the problem of a model calibration). The formulas allowing for any f to calculate the joint distribution of vertex degrees at the ends of randomly selected edge are also obtained. Some other results are presented in the paper.

Suggested Citation

  • Zadorozhnyi, V.N. & Yudin, E.B., 2015. "Growing network: Models following nonlinear preferential attachment rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 111-132.
    • Handle: RePEc:eee:phsmap:v:428:y:2015:i:c:p:111-132
    DOI: 10.1016/j.physa.2015.01.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115000710
    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.2015.01.052?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. Safaei, F. & Yeganloo, H. & Akbar, R., 2020. "Robustness on topology reconfiguration of complex networks: An entropic approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 379-409.
    2. Li, Bo & Sun, Duoyong & Bai, Guanghan, 2017. "Empirical research on evolutionary behavior of covert network with preference measurement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 33-43.
    3. Liao, Fuxuan & Hayashi, Yukio, 2022. "Emergence of robust and efficient networks in a family of attachment models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(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:428:y:2015:i:c:p:111-132. 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.