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

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  • 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
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    References listed on IDEAS

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    1. Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
    2. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    3. Pengsheng Ji & Jiashun Jin & Zheng Tracy Ke & Wanshan Li, 2022. "Co-citation and Co-authorship Networks of Statisticians," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 469-485, April.
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