Identification of core sub-team on scientific collaboration networks with Shapley method
Identifying the core sub-teams that drive productivity in scientific collaboration networks is essential for research evaluation and team management. However, existing methods typically rank individual researchers by bibliometric impact or select structurally cohesive clusters, but rarely account fo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-07-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-3048.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849245404093218816 |
|---|---|
| author | Lixin Zhou Chen Liu Xue Song |
| author_facet | Lixin Zhou Chen Liu Xue Song |
| author_sort | Lixin Zhou |
| collection | DOAJ |
| description | Identifying the core sub-teams that drive productivity in scientific collaboration networks is essential for research evaluation and team management. However, existing methods typically rank individual researchers by bibliometric impact or select structurally cohesive clusters, but rarely account for both collaboration patterns and joint scientific output. To address this limitation, we propose a novel two-dimensional framework that integrates network topology with research performance to identify core sub-teams. Specifically, we measure each sub-team’s marginal structural contribution using the Shapley value and quantify its collective impact using a sub-team H-index. To efficiently identify high-contributing sub-teams, we employ the Monte Carlo Tree Search algorithm, along with an approximation strategy to estimate Shapley values under computational constraints. We evaluate our method on 61 real-world scientific collaboration teams from Web of Science and Baidu Scholar data. Experimental results validate the effectiveness of our method in identifying core sub-teams, with the highest collaborative and citation impact. The proposed method offers a valuable analytical tool for research managers and funding agencies seeking to locate high-impact collaborative clusters, and it provides a generalizable framework for studies requiring the integration of structural and performance-based indicators in network analysis. |
| format | Article |
| id | doaj-art-ceadec0cf4804881a4199ff3dfbcbb64 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-ceadec0cf4804881a4199ff3dfbcbb642025-08-20T03:58:49ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e304810.7717/peerj-cs.3048Identification of core sub-team on scientific collaboration networks with Shapley methodLixin Zhou0Chen Liu1Xue Song2School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaIdentifying the core sub-teams that drive productivity in scientific collaboration networks is essential for research evaluation and team management. However, existing methods typically rank individual researchers by bibliometric impact or select structurally cohesive clusters, but rarely account for both collaboration patterns and joint scientific output. To address this limitation, we propose a novel two-dimensional framework that integrates network topology with research performance to identify core sub-teams. Specifically, we measure each sub-team’s marginal structural contribution using the Shapley value and quantify its collective impact using a sub-team H-index. To efficiently identify high-contributing sub-teams, we employ the Monte Carlo Tree Search algorithm, along with an approximation strategy to estimate Shapley values under computational constraints. We evaluate our method on 61 real-world scientific collaboration teams from Web of Science and Baidu Scholar data. Experimental results validate the effectiveness of our method in identifying core sub-teams, with the highest collaborative and citation impact. The proposed method offers a valuable analytical tool for research managers and funding agencies seeking to locate high-impact collaborative clusters, and it provides a generalizable framework for studies requiring the integration of structural and performance-based indicators in network analysis.https://peerj.com/articles/cs-3048.pdfScientific collaboration networksShapley value analysisCore sub-teamMCTS |
| spellingShingle | Lixin Zhou Chen Liu Xue Song Identification of core sub-team on scientific collaboration networks with Shapley method PeerJ Computer Science Scientific collaboration networks Shapley value analysis Core sub-team MCTS |
| title | Identification of core sub-team on scientific collaboration networks with Shapley method |
| title_full | Identification of core sub-team on scientific collaboration networks with Shapley method |
| title_fullStr | Identification of core sub-team on scientific collaboration networks with Shapley method |
| title_full_unstemmed | Identification of core sub-team on scientific collaboration networks with Shapley method |
| title_short | Identification of core sub-team on scientific collaboration networks with Shapley method |
| title_sort | identification of core sub team on scientific collaboration networks with shapley method |
| topic | Scientific collaboration networks Shapley value analysis Core sub-team MCTS |
| url | https://peerj.com/articles/cs-3048.pdf |
| work_keys_str_mv | AT lixinzhou identificationofcoresubteamonscientificcollaborationnetworkswithshapleymethod AT chenliu identificationofcoresubteamonscientificcollaborationnetworkswithshapleymethod AT xuesong identificationofcoresubteamonscientificcollaborationnetworkswithshapleymethod |