ASU Electronic Theses and Dissertations
- 2 English
- 2 Public
Graphs are commonly used visualization tools in a variety of fields. Algorithms have been proposed that claim to improve the readability of graphs by reducing edge crossings, adjusting edge length, or some other means. However, little research has been done to determine which of these algorithms best suit human perception for particular graph properties. This thesis explores four different graph properties: average local clustering coefficient (ALCC), global clustering coefficient (GCC), number of triangles (NT), and diameter. For each of these properties, three different graph layouts are applied to represent three different approaches to graph visualization: multidimensional scaling (MDS), force directed …
- Clayton, Benjamin, Maciejewski, Ross, Kobourov, Stephen, et al.
- Created Date
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 …
- Soni, Utkarsh, Maciejewski, Ross, Kobourov, Stephen, et al.
- Created Date