摘要
[目的/意义]对人工智能领域科研团队进行识别,并基于多个维度的指标提取领军科研团队,旨在丰富科研团队识别的流程与方法,为从科研团队视角分析人工智能领域脉络、前沿和主题提供依据。[方法/过程]以Web of Science为数据来源,采集2009-2018年间人工智能学科领域所有科技论文的数据,通过算法设计与人工核查进行数据清洗;基于分数计数法构建全局合著网络,并利用社区探测算法动态调参、识别科研团队;进而基于多维度的指标提取出领军团队,并加以比较分析。[结果/结论]从实践出发构造人工智能科技论文数据清洗的规则;构建基于合著关系识别人工智能科研团队的流程体系;提出通过消除边缘结点进行合著网络筛选,进而利用已知团队作为参考进行参数调整的思路;较为系统和准确地识别出全球人工智能科研团队,并基于发文量、被引量、h指数、中介中心度、接近中心度和加权点度中心度6个维度的指标提取出领军科研团队,同时,给出结合论文数据和实证调研对每个领军团队的示例性分析。
[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.
作者
余厚强
白宽
邹本涛
王曰芬
Yu Houqiang;Bai Kuan;Zou Bentao;Wang Yuefen(School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094)
出处
《图书情报工作》
CSSCI
北大核心
2020年第20期3-13,共11页
Library and Information Service
基金
国家社会科学基金重大项目"面向知识创新服务的数据科学理论与方法研究"(项目编号16ZAD224)研究成果之一。
关键词
人工智能
合著网络
科研团队
领军团队
数据分析
artificial intelligence
co-authorship network
research team
leading research team
data analysis
