IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i8d10.1007_s11192-024-05092-2.html
   My bibliography  Save this article

Weighted degrees and truncated derived bibliographic networks

Author

Listed:
  • Vladimir Batagelj

    (IMFM - Institute of Mathematics, Physics and Mechanics
    UP IAM - University of Primorska)

Abstract

Large bibliographic networks are sparse—the average node degree is small. This does not necessarily apply to their product—in some cases, it can “explode” (not sparse, increasing in temporal and spatial complexity). An approach in such cases is to reduce the complexity of the problem by restricting our attention to a selected subset of important nodes and computing with corresponding truncated networks. Nodes can be selected based on various criteria. An option is to consider the most important nodes in the derived network—the nodes with the largest weighted degree. We show that the weighted degrees in a derived network can be efficiently computed without computing the derived network itself, and elaborate on this scheme in detail for some typical cases.

Suggested Citation

  • Vladimir Batagelj, 2024. "Weighted degrees and truncated derived bibliographic networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4863-4883, August.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:8:d:10.1007_s11192-024-05092-2
    DOI: 10.1007/s11192-024-05092-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-024-05092-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-024-05092-2?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.

    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:spr:scient:v:129:y:2024:i:8:d:10.1007_s11192-024-05092-2. 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.springer.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.