[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.
Yu Houqiang
,
Bai Kuan
,
Zou Bentao
,
Wang Yuefen
. Identification and Extraction of Research Team in the Artificial Intelligence Field[J]. Library and Information Service, 2020
, 64(20)
: 4
-13
.
DOI: 10.13266/j.issn.0252-3116.2020.20.001
[1] 陈春花,杨映珊.基于团队运作模式的科研管理研究[J].科技进步与对策,2002(4):79-81.
[2] ACEDO F J, BARROSO C, CASANUEVA C, et al. Co-authorship in management and organizational studies:an empirical and network analysis[J]. Journal of management studies, 2006, 43(5):957-983.
[3] GREGORIO G, JINSEO P, CHARLES H, et al. Scientific authorships and collaboration network analysis on chagas disease:papers indexed in pubmed (1940-2009)[J]. Journal of the institute of tropical medicine in sao paulo, 2012, 54(4):219-228.
[4] 李亮, 朱庆华. 社会网络分析方法在合著分析中的实证研究[J].情报科学,2008, 26(4):549-555.
[5] 李纲,李春雅,李翔. 基于社会网络分析的科研团队发现研究[J].图书情报工作,2014,58(7):63-70,82.
[6] 沈耕宇,黄水清,王东波. 以作者合作共现为源数据的科研团队发掘方法研究[J].数据分析与知识发现, 2013,29(1):57-62.
[7] 吕璐成,赵亚娟,王学昭,等. 基于关联规则挖掘的研发团队识别方法[J].科技管理研究, 2016, 36(17):148-152,189.
[8] ANTONIO P, CARLOS O, FÉLIX M. Detecting, identifying and visualizing research groups in co-authorship networks[J]. Scientometrics, 2010, 82(2):307-319.
[9] 任妮, 周建农. 合著网络加权模式下科研团队的发现与评价研究[J].现代图书情报技术, 2015,31(9):68-75.
[10] 范丽鹏,余厚强,姜宇星,等.人工智能研究前沿识别与分析:基于高产机构对比研究视角[J].情报理论与实践,2019,42(9):16-21.
[11] TRAN H N, HUYNH T, DO T. Author name disambiguation by using deep neural network[C]//Asian conference on intelligent information and database systems. Phuket, Thailand:ACIIDS, 2014:123-132.
[12] GLANZEL W. National characteristics in international scientific co-authorship relations[J]. Scientometrics, 2001, 51(1):69-115.
[13] PRITYCHENKO B. Fractional authorship in nuclear physics[J]. Scientometrics, 2016, 106(1):461-468.
[14] 许治,陈丽玉,王思卉.高校科研团队合作程度影响因素研究[J].科研管理,2015,36(5):149-161.