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

Tong Zhou, Rui Pan, Junfei Zhang () and Hansheng Wang
Additional contact information
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

Computational Statistics, 2025, vol. 40, issue 1, No 8, 177-204

Abstract: 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.

Keywords: Node2Vec; Node attribute; Dynamic community detection; Co-authorship network (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-024-01486-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01486-1

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-024-01486-1

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().