The State of the Art in Empirical User Evaluation of Graph Visualizations

Michael Burch,Daniel Weiskopf,Weidong Huang,Mathew Wakefield,Jie Hua,Helen C Purchase

doi:10.1109/access.2020.3047616

Abstract

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this article, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation, not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions.

Highlights

Graph visualization and graph drawing have become frequently studied fields of research [1], [2]
In this article, we presented the state of the art in empirical user evaluation of graph visualizations
While there is a large body of work on graph interpretation, in particular, on graph layouts and the aesthetics of those drawings, as well as dynamic graph visualization evaluation, only a few approaches exist on the memorization of graph visualizations and on how people create graphs, i.e., how graphs are taught and how well people perform when learning graph visualizations

Summary

INTRODUCTION

Graph visualization and graph drawing have become frequently studied fields of research [1], [2]. One reason for the increased focus in these fields is the variety of applications that must deal with relational information such as coupling data in software development, protein interactions in bioinformatics, contacts between people in social networking, or schematic maps in the field of cartography or public transportation. In some scenarios, it is not just the relational information given in a dataset that needs to be visualized, and the weight, multitude, or direction of relations.

BACKGROUND

SCOPE AND METHODOLOGY

CLASSIFICATION OF EMPIRICAL USER EVALUATION IN GRAPH VISUALIZATION

OPEN RESEARCH CHALLENGES AND WHITE SPOTS

Findings

CONCLUSION