Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Teamwork has become an important form of organization for knowledge innovation today. Exploring the dynamic evolution law of scientific research teams from the perspective of dynamic networks is of great significance to promote the discovery, formation and management of scientific research teams.[Method/process] Taking the field of artificial intelligence as an example, this paper used the Louvain community discovery algorithm to identify research teams in the field of artificial intelligence. The extreme value distribution of the number of nodes, edges, network density, and clustering coefficients in the team cooperation network were calculated. A combination of micro and macro perspective explored the laws and characteristics of the evolution of high-yield teams in this field, aiming at revealing the intrinsic motivation of the evolution of scientific research teams.[Result/conclusion] From the micro perspective, the extreme value distribution of co-authored network topological indicators reveals the dynamic properties of the evolution of high-yield teams in the field of artificial intelligence; from the macro perspective, high-yield teams show evolutionary commonality in network density and network average clustering coefficients, and most teams foster more new cooperative relationships in the evolution process. In view of the evolution path of the team, the phenomenon of "small group" cooperation in high-yield teams in the field of artificial intelligence is significant, and the cooperation between "small groups" directly affects the direction of the overall team.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] This paper identifies the research team in the artificial intelligence field, and extracts the leading research team from multi-dimensional indicators, aiming to enrich the process and method of identification of the research team, and provide the basis for analyzing the context, frontier and theme of the field of artificial intelligence from the perspective of the research team.[Method/process] This paper was based on the publication data of the Web of Science category Computer Science, Artificial Intelligence from 2009 to 2018, and did data cleaning via programming and manual check. Global co-author network is constructed based on the fractional counting method, and the Louvain algorithm was used to dynamically tune and identify the research teams. Moreover, the leading research team was extracted based on different indicators with parameter adjustment.[Result/conclusion] From practical view, the study has constructed a set of rules for cleaning publication data of artificial intelligence field. The process of identifying artificial intelligence research teams based on co-authorship is constructed. The study proposes the method of tuning the parameter by eliminating edge nodes in the collaboration network and further taking the known research teams as baseline. The worldwide research teams of artificial intelligence field are systematically and accurately identified. The leading research teams are further extracted based on indicators of six dimensions, i.e. number of publications、number of citations、h index、weighted degree centrality、betweenness centrality、closeness centrality. Exemplary analysis is conducted on leading research teams of each dimension by combining the publication data and web information survey.
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