Higher-order structures of local collaboration networks are associated with individual scientific productivity
Abstract The prevalence of teamwork in contemporary science has raised new questions about collaboration networks and the potential impact on research outcomes. Previous studies primarily focused on pairwise interactions between scientists when constructing collaboration networks, potentially overlo...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-02-01
|
| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-024-00453-6 |
| _version_ | 1797275485406756864 |
|---|---|
| author | Wenlong Yang Yang Wang |
| author_facet | Wenlong Yang Yang Wang |
| author_sort | Wenlong Yang |
| collection | DOAJ |
| description | Abstract The prevalence of teamwork in contemporary science has raised new questions about collaboration networks and the potential impact on research outcomes. Previous studies primarily focused on pairwise interactions between scientists when constructing collaboration networks, potentially overlooking group interactions among scientists. In this study, we introduce a higher-order network representation using algebraic topology to capture multi-agent interactions, i.e., simplicial complexes. Our main objective is to investigate the influence of higher-order structures in local collaboration networks on the productivity of the focal scientist. Leveraging a dataset comprising more than 3.7 million scientists from the Microsoft Academic Graph, we uncover several intriguing findings. Firstly, we observe an inverted U-shaped relationship between the number of disconnected components in the local collaboration network and scientific productivity. Secondly, there is a positive association between the presence of higher-order loops and individual scientific productivity, indicating the intriguing role of higher-order structures in advancing science. Thirdly, these effects hold across various scientific domains and scientists with different impacts, suggesting strong generalizability of our findings. The findings highlight the role of higher-order loops in shaping the development of individual scientists, thus may have implications for nurturing scientific talent and promoting innovative breakthroughs. |
| first_indexed | 2024-03-07T15:15:03Z |
| format | Article |
| id | doaj.art-6e345fa428fc4c209dc4eb0348d697cc |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-03-07T15:15:03Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-6e345fa428fc4c209dc4eb0348d697cc2024-03-05T17:58:40ZengSpringerOpenEPJ Data Science2193-11272024-02-0113112210.1140/epjds/s13688-024-00453-6Higher-order structures of local collaboration networks are associated with individual scientific productivityWenlong Yang0Yang Wang1School of Public Policy and Administration, Xi’an Jiaotong UniversitySchool of Public Policy and Administration, Xi’an Jiaotong UniversityAbstract The prevalence of teamwork in contemporary science has raised new questions about collaboration networks and the potential impact on research outcomes. Previous studies primarily focused on pairwise interactions between scientists when constructing collaboration networks, potentially overlooking group interactions among scientists. In this study, we introduce a higher-order network representation using algebraic topology to capture multi-agent interactions, i.e., simplicial complexes. Our main objective is to investigate the influence of higher-order structures in local collaboration networks on the productivity of the focal scientist. Leveraging a dataset comprising more than 3.7 million scientists from the Microsoft Academic Graph, we uncover several intriguing findings. Firstly, we observe an inverted U-shaped relationship between the number of disconnected components in the local collaboration network and scientific productivity. Secondly, there is a positive association between the presence of higher-order loops and individual scientific productivity, indicating the intriguing role of higher-order structures in advancing science. Thirdly, these effects hold across various scientific domains and scientists with different impacts, suggesting strong generalizability of our findings. The findings highlight the role of higher-order loops in shaping the development of individual scientists, thus may have implications for nurturing scientific talent and promoting innovative breakthroughs.https://doi.org/10.1140/epjds/s13688-024-00453-6Higher-order structuresLocal collaboration networksDisconnected componentsHigher-order loopsProductivity |
| spellingShingle | Wenlong Yang Yang Wang Higher-order structures of local collaboration networks are associated with individual scientific productivity EPJ Data Science Higher-order structures Local collaboration networks Disconnected components Higher-order loops Productivity |
| title | Higher-order structures of local collaboration networks are associated with individual scientific productivity |
| title_full | Higher-order structures of local collaboration networks are associated with individual scientific productivity |
| title_fullStr | Higher-order structures of local collaboration networks are associated with individual scientific productivity |
| title_full_unstemmed | Higher-order structures of local collaboration networks are associated with individual scientific productivity |
| title_short | Higher-order structures of local collaboration networks are associated with individual scientific productivity |
| title_sort | higher order structures of local collaboration networks are associated with individual scientific productivity |
| topic | Higher-order structures Local collaboration networks Disconnected components Higher-order loops Productivity |
| url | https://doi.org/10.1140/epjds/s13688-024-00453-6 |
| work_keys_str_mv | AT wenlongyang higherorderstructuresoflocalcollaborationnetworksareassociatedwithindividualscientificproductivity AT yangwang higherorderstructuresoflocalcollaborationnetworksareassociatedwithindividualscientificproductivity |