Representing asymmetric relationships by h-plots. Discovering the archetypal patterns of cross-journal citation relationships

TL;DR

This paper introduces a scalable h-plot based method for visualizing asymmetric relationships, such as cross-journal citations, by embedding variables rather than objects, enabling interpretable archetypal analysis.

Contribution

The novel approach embeds variables to visualize asymmetric proximity data and applies archetypoid analysis for identifying extreme journal profiles.

Findings

Effective visualization of asymmetric citation relationships

Successful identification of archetypal journals

Comparable or superior performance to existing methods

Abstract

This work approaches the multidimensional scaling problem from a novel angle. We introduce a scalable method based on the h-plot, which inherently accommodates asymmetric proximity data. Instead of embedding the objects themselves, the method embeds the variables that define the proximity to or from each object. It is straightforward to implement, and the quality of the resulting representation can be easily evaluated. The methodology is illustrated by visualizing the asymmetric relationships between the citing and cited profiles of journals on a common map. Two profiles that are far apart (or close together) in the h-plot, as measured by Euclidean distance, are different (or similar), respectively. This representation allows archetypoid analysis (ADA) to be calculated. ADA is used to find archetypal journals (or extreme cases). We can represent the dataset as convex combinations of these archetypal journals, making the results easy to interpret, even for non-experts. Comparisons with other methodologies are carried out, showing the good performance of our proposal. Code and data are available for reproducibility.