The Perception of Graph Properties in Graph Layouts |
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Authors: | Utkarsh Soni Yafeng Lu Brett Hansen Helen C. Purchase Stephen Kobourov Ross Maciejewski |
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Affiliation: | 1. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, US;2. School of Computing Science, University of Glasgow, Scotland;3. Department of Computer Science, University of Arizona, US |
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Abstract: | When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user's ability to perceive graph properties for a given graph layout. In this study, we apply previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. We conduct a large scale (n = 588) crowdsourced experiment to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber's law. We study three graph layout algorithms from three representative classes (Force Directed ‐ FD, Circular, and Multi‐Dimensional Scaling ‐ MDS), and the results of this experiment establish the precision of judgment for these graph layouts and properties. Our findings demonstrate that the perception of graph density can be modeled with Weber's law. Furthermore, the perception of the average clustering coefficient can be modeled as an inverse of Weber's law, and the MDS layout showed a significantly different precision of judgment than the FD layout. |
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