Structure Based Aesthetics and Support of Cognitive Tasks for Graph Evaluation
This addresses the challenge of improving graph comprehension for users in fields like data visualization, though it appears incremental as it builds on existing aesthetics and evaluation methods.
The paper tackles the problem that existing graph drawing aesthetics often fail to reliably reveal structural features and are not evaluated for complex tasks, proposing to derive aesthetics from graph structure and apply cognitive load theory for evaluation.
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.