关键词: 作者共引分析, 共引分析, 引文分析, 信息计量学, 比较研究

Abstract:

Traditional author co-citation analysis (ACA) firstly calculates cocitation relationship between two authors, after which researchers can transform raw cocitation matrix and do some further analysis to map knowledge domain. Nevertheless, such method has been criticized a lot since its input is less informative. Since time of cited paper`s publication (TofC) can, to some extent, emphasize the weight of author cocitation relationship, this paper puts TofC into our algorithm to strengthen traditional ACA methods so that the relationship on author co-citation can be closer and that knowledge mapping can be more relevant. Such method uses natural logarithm model. This paper will construct a co-citation matrix by calculating distinct weights on both difference between TofC and raw author co-citation relationship, and explains results after transforming matrix and multianalysis. Results show that there is more clustering effect when adding TofC, which actually improves the visualization of knowledge mapping. What`s more, it mines more details than traditional ACA methods.

Key words: Author co-citation analysis, Co-citation analysis, Citation analysis, Informetrics, Comparative study