Ding Ying
School of Library and Information Science, Indiana University, Bloomington, IN, 47405.
J Informetr. 2011 Jan 1;5(1):187-203. doi: 10.1016/j.joi.2010.10.008.
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.
科学合作与认可都是成熟的研究主题，采用三种方法：调查/问卷法、文献计量学和复杂网络分析。本文结合主题建模和路径寻找算法，以确定高产作者是否倾向于与具有相同或不同兴趣的研究人员合作或引用他们的文献，以及高被引作者是否倾向于相互合作或引用彼此的文献。以信息检索作为测试领域，结果表明，高产作者倾向于与具有相同研究兴趣的同事直接共同撰写论文并密切引用他们的文献；他们一般不会直接与研究主题不同的同事合作，而是直接或间接地引用他们；高被引作者一般不会相互共同撰写论文，但会密切引用彼此的文献。
