Full metadata
Title
The Perception of Graph Properties In Graph Layouts
Description
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. This study applies previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. A large scale (n = 588) crowdsourced experiment is conducted to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber’s law. Three graph layout algorithms from three representative classes (Force Directed - FD, Circular, and Multi-Dimensional Scaling - MDS) are studied, and the results of this experiment establish the precision of judgment for these graph layouts and properties. The 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.
Date Created
2018
Contributors
- Soni, Utkarsh (Author)
- Maciejewski, Ross (Thesis advisor)
- Kobourov, Stephen (Committee member)
- Sefair, Jorge (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
92 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.50484
Level of coding
minimal
Note
Masters Thesis Computer Science 2018
System Created
- 2018-10-01 08:01:41
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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