图书情报知识 ›› 2015, Vol. 0 ›› Issue (6): 89-97.doi: 10.13366/j.dik.2015.06.089

• 情报、信息与共享 • 上一篇    下一篇

优化传统作者共引分析的研究初探——综合引文发表时间信息的作者共引分析方法

步一，刘天祎，黄文彬   

Exploration on Research of Improving Traditional Author Co-citation Analysis——A Novel Author Co-citation Analysis Method Combining with Publishing Time of Cited Papers

摘要：

传统作者共引分析方法通过计算文献作者间共被引关系构建共引矩阵，进而将共引矩阵转化分析后绘制该领域的知识图谱。然而这种方法由于输入信息量过小而饱受诟病。由于不同引文间的发表时间在一定程度上可以凸显作者间的被引强度关系，本文利用引文发表时间信息强化传统的算法使作者共引关系更紧密，以得出更具有参考性的知识图谱。这种方法使用了自然对数模型。笔者将引文间发表时间差距与原始作者共引关系依不同权重计算带有时间信息的共引矩阵，并通过矩阵转化和多元分析后进行结果分析与解释。结果显示，加入引文发表时间信息不仅提高了作者共引分析的聚类结果的群聚性和知识图谱的可视化程度，而且可以挖掘出比传统方法更多的细节。

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
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