A comparison of mapping algorithms for author co-citation data analysis

Abstract

A key process of any citation analysis study is to map the coded citation data from a high-dimensional dataset to a lower dimensional one while detecting the groups, clusters, patterns or other features of the citation relationships. Over the years, many methods have been used in various studies, including multi-dimensional scaling, Pathfinder networks, Kohonen's self-organizing mapping, etc. Many of these methods are fundamentally different, but their results are similar and comparable. In this study, we selected and applied four of the mapping methods to the same dataset, the author co-citation matrix of the top 100 highly cited information scientists. The results of the different mapping methods provide interesting comparisons among the different mapping algorithms as well as the different views of the dataset.