Collaborative Team Recognition: A Core Plus Extension Structure. Issue 4 (November 2022)
- Record Type:
- Journal Article
- Title:
- Collaborative Team Recognition: A Core Plus Extension Structure. Issue 4 (November 2022)
- Main Title:
- Collaborative Team Recognition: A Core Plus Extension Structure
- Authors:
- Yu, Shuo
Alqahtani, Fayez
Tolba, Amr
Lee, Ivan
Jia, Tao
Xia, Feng - Abstract:
- Highlights: 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. Abstract: 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 withHighlights: 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. Abstract: 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. … (more)
- Is Part Of:
- Journal of informetrics. Volume 16:Issue 4(2022)
- Journal:
- Journal of informetrics
- Issue:
- Volume 16:Issue 4(2022)
- Issue Display:
- Volume 16, Issue 4 (2022)
- Year:
- 2022
- Volume:
- 16
- Issue:
- 4
- Issue Sort Value:
- 2022-0016-0004-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11
- Subjects:
- Collaborative teams -- Scientific collaboration -- Social network analysis -- Team science -- Scholarly big data
Library statistics -- Periodicals
Information science -- Statistical methods -- Periodicals
Bibliometrics -- Periodicals
Bibliothèques -- Statistiques -- Périodiques
Sciences de l'information -- Méthodes statistiques -- Périodiques
Bibliométrie -- Périodiques
020.727 - Journal URLs:
- http://www.journals.elsevier.com/journal-of-informetrics/ ↗
http://rave.ohiolink.edu/ejournals/issn/17511577/ ↗
http://www.sciencedirect.com/science/journal/17511577 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.joi.2022.101346 ↗
- Languages:
- English
- ISSNs:
- 1751-1577
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5006.830000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24377.xml