Collaborative Team Recognition: A Core Plus Extension Structure |
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| Institution: | 1. School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China;2. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia;3. Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia;4. STEM, University of South Australia, Adelaide, SA 5001, Australia;5. College of Computer and Information Science, Southwest University, Chongqing 400715, China;6. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3353, Australia;1. School of Information Management, Nanjing University, Nanjing, China;2. Centre for R&D Monitoring (ECOOM) and Department MSI, KU Leuven, Belgium;3. Faculty of Social Sciences, University of Antwerp, Belgium;4. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China;1. School of Information Management, Nanjing University, Nanjing 210032, China;2. Department of Information Management, Peking University, Beijing 100871, China;1. School of Management Science and Engineering, Central University of Finance and Economics, 39 South College Road, Haidian District, Beijing 100081, PR China;2. CAS Center for Interdisciplinary Studies of Social and Natural Sciences, Chinese Academy of Sciences, No.15 ZhongGuanCunBeiYiTiao Alley, Haidian District, Beijing 100090, PR China;3. Institutes of Science and Development, CAS, No.15 ZhongGuanCunBeiYiTiao Alley, Haidian District, Beijing 100190, PR China;4. School of Public Policy and Management, University of Chinese Academy of Sciences, No.19A Yuquanlu, Beijing 100049, PR China;1. School of Information Management, Sun Yat-Sen University, Guangzhou, 510006, PR. China;2. School of Economics & Management, Nanjing University of Science and Technology, Nanjing, 210034, PR. China;3. Library, Party School of Jiangsu Provincial Committee of CPC, Nanjing, 210034, PR. China |
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| 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. |
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