Structure Based Aesthetics and Support of Cognitive Tasks for Graph Evaluation

TL;DR

This paper proposes structure-based aesthetics for graph drawing to better reveal structural features and explores evaluation methods from Cognitive Load Theory to assess their effectiveness in supporting complex cognitive tasks.

Contribution

It introduces a novel approach to derive aesthetics from graph structural features and discusses evaluation methodologies for complex tasks based on Cognitive Load Theory.

Findings

Aesthetics derived from graph structure may improve structural feature visibility.

Evaluation of aesthetics on complex tasks remains an open research area.

Applying Cognitive Load Theory offers new insights into graph evaluation.

Abstract

Drawing principles, or aesthetics, are important in graph drawing. They are used as criteria for algorithm design and for quality evaluation. Current aesthetics are described as visual properties that a drawing is required to have to be visually pleasing. However, most of these aesthetics are originally proposed without consideration of graph structure information. Therefore their ability in visually revealing graph structural features are not guaranteed and indeed mixed results have been reported in the literature regarding their impact on user graph comprehension. In this paper, we propose to derive aesthetics based on graph internal structural features. Further, graphs are often evaluated based on controlled experiments with simple perception tasks to avoid possible confounding factors caused by complex tasks. This leaves their value in supporting complex tasks unevaluated. To fill this gap, we also discuss the possibility of applying evaluation methodologies used in the Cognitive Load Theory research for graph evaluation.