Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks

被引:297
作者
Ding, Ying [1 ]
机构
[1] Indiana Univ, Sch Lib & Informat Sci, Bloomington, IN 47405 USA
关键词
Scientific collaboration; Scientific endorsement; Topic modeling; Path-finding algorithm; CO-AUTHORSHIP NETWORKS; INFORMATION-RETRIEVAL; COCITATION ANALYSIS; SOCIAL NETWORKS; SCIENCE; IMPACT; CLASSIFICATION;
D O I
10.1016/j.joi.2010.10.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Scientific collaboration and endorsement are well-established research topics which utilize three kinds of methods: survey/questionnaire, bibliometrics, and complex network analysis. This paper combines topic modeling and path-finding algorithms to determine whether productive authors tend to collaborate with or cite researchers with the same or different interests, and whether highly cited authors tend to collaborate with or cite each other. Taking information retrieval as a test field, the results show that productive authors tend to directly coauthor with and closely cite colleagues sharing the same research interests; they do not generally collaborate directly with colleagues having different research topics, but instead directly or indirectly cite them; and highly cited authors do not generally coauthor with each other, but closely cite each other. Published by Elsevier Ltd.
引用
收藏
页码:187 / 203
页数:17
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