Co-authorship network and the correlation with academic performance


This paper aims to study the internal structure of the co-authorship network and the relationship between the network and the authors’ academic performance in the network. In order to conduct this research, bibliographic data of 166 authors from three top higher education institutions of Shanghai was collected and the method of social network analysis (SNA) was performed to analyze the data. In the link analysis, the centrality, egocentric network efficiency, authorities, and hubs were analyzed. In the graph cluster analysis, this paper employs clustering algorithms based on betweenness. Lastly, the Spearman correlation test was performed to analyze the relationship between academic performance and SNA metrics. This paper found that and betweenness centrality, eigenvector centrality, authority and hub position, and efficiency were significant to g-index. The research provided a glimpse of the co-authorship network's internal structure in China. Additionally, the SNA method of identifying productive scholars can also be applied to other areas, such as the network of equipment in the Industry 5.0 to help companies identify the strong and weak links in the producing process.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Funding Information: This work is partly supported by VC Research ( VCR 0000085 ).
Uncontrolled Keywords: Big data analytics,Co-authorship network,Graph cluster analysis,Internet of things (IoT),Link analysis,Social network analysis,Spearman correlation test,Artificial Intelligence,Computer Science Applications,Information Systems,Software,Hardware and Architecture,Computer Science (miscellaneous),Management of Technology and Innovation,Engineering (miscellaneous)
Publication ISSN: 2542-6605
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 1396?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2020-12-01
Accepted Date: 2020-10-10
Authors: Ariel Xu, Qianwen
Chang, Victor (ORCID Profile 0000-0002-8012-5852)

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