Visualizing Proximity Data

Abstract

In this article, the authors explore the use of graph layout algorithms for visualizing proximity matrices such as those obtained in cultural domain analysis. Traditionally, multidimensional scaling has been used for this purpose. The authors compare the two approaches to identify conditions when each approach is effective. As might be expected, they find that multidimensional scaling shines when the data are of low dimensionality and are compatible with the defining characteristics of Euclidean distances, such as symmetry and triangle inequality constraints. However, when one is working with data that do not fit these criteria, graph layout algorithms do a better job of communicating the structure of the data. In addition, graph layout algorithms lend themselves to interactive use, which can yield a deeper and more accurate understanding of the data.