IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i1d10.1007_s00180-024-01486-1.html
   My bibliography  Save this article

An attribute-based Node2Vec model for dynamic community detection on co-authorship network

Author

Listed:
  • Tong Zhou

    (Central University of Finance and Economics)

  • Rui Pan

    (Central University of Finance and Economics)

  • Junfei Zhang

    (Central University of Finance and Economics)

  • Hansheng Wang

    (Peking University)

Abstract

Networks offer a wide range of applications in various domains of life and scientific research. Community detection, which aims at understanding the structure and function of complex networks, is a basic and essential task in network analysis. In this study, we propose an approach for community detection in a dynamic network based on network embedding, incorporating both network topology and node attributes. Furthermore, we analyze the evolution of statistician collaborative patterns and statistical research topics based on dynamic co-authorship networks through publications that are collected from 43 statistical journals from 2001 to 2021. Specifically, we explore the dynamic community detection results based on the newly proposed approach and conduct statistical analysis from the following perspectives. First, the evolution information of the community center is mined. Second, we explore the collaboration mode of community institutions. Finally, we track the evolution of community research content. This study provides a novel method for exploring network representation with node attributes and the analysis of dynamic community detection, as well as offers multiple perspectives for community detection analysis.

Suggested Citation

  • Tong Zhou & Rui Pan & Junfei Zhang & Hansheng Wang, 2025. "An attribute-based Node2Vec model for dynamic community detection on co-authorship network," Computational Statistics, Springer, vol. 40(1), pages 177-204, January.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01486-1
    DOI: 10.1007/s00180-024-01486-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01486-1
    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/s00180-024-01486-1?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:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01486-1. 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.