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Collaborative Team Recognition: A Core Plus Extension Structure.

Authors :
Yu, Shuo
Alqahtani, Fayez
Tolba, Amr
Lee, Ivan
Jia, Tao
Xia, Feng
Source :
Journal of Informetrics; Nov2022, Vol. 16 Issue 4, pN.PAG-N.PAG, 1p
Publication Year :
2022

Abstract

• A fine-grained collaborative team recognition method is proposed. • Collaborative teams are formulated with "core+extension" structure. • The underlying relationship between team output and collaboration intensity is found. • It is found that core members have broad collaboration relationships and fixed collaboration patterns. Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension" team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17511577
Volume :
16
Issue :
4
Database :
Supplemental Index
Journal :
Journal of Informetrics
Publication Type :
Academic Journal
Accession number :
160397756
Full Text :
https://doi.org/10.1016/j.joi.2022.101346