Yi Bu，Tian-yi Liu，Win-bin Huang.MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations.Scientometrics (2016) 108:143–166

Abstract

Author co-citation analysis (ACA) is a well-known and frequently-used method to exhibit the academic researchers and the professional field sketch according to cocitation relationships between authors in an article set. However, visualizing subtle examination is limited because only author co-citation information is required in ACA.The proposed method, called modified author co-citation analysis (MACA), exploits author co-citation relationship, citations published time, citations published carriers, and citations keywords, to construct MACA-based co-citation matrices. According to the results of our experiments: (1) MACA shows a good clustering result with more delicacy and more clearness; (2) more information involved in co-citation analysis performs good visual acuity; (3) in visualization of co-citation network produced by MACA, the points in different categories have far more distance, and the points indicating authors in the same category are closer together. As a result, the proposed MACA is found that more detailed and subtle information of a knowledge domain analyzed can be obtained, compared to ACA.

Keywords：Author co-citation analysis  Co-citation analysis  Citation analysis Bibliometrics

作者共引分析是根据一个论文集当中作者之间的引用关系来刻画研究人员及其研究领域的一种常用的方法。但是由于其只是考虑的作者共引信息而使其可视化的信息比较有有限。本文提出的MACA方法，把作者共引关系，引文的时间信息，引文出版载体和引文关键词等信息综合考虑来构建共引矩阵。从实验结果看，（1）MACA显示了更加精致和更加清晰的聚类效果；（2）共引分析中有更加丰富的信息；（3）在共引网络图当中，不同类别的信息在共引网络图中距离更远，同一类别的信息距离更近。总的来看，MACA方法比原来的ACA能够更加精细的敏锐地反映知识领域。