Name
Abstract | ||
|---|---|---|
Analyzing network data can provide valuable insights in many diverse fields. However, designing node-link visualizations that effectively communicate the underlying network is challenging, as for every network there are many potential unintelligible or even misleading layouts. Automated layout algorithms have helped, but frequently generate ineffective visualizations. In order to build awareness of effective node-link visualization strategies, we detail new global readability metrics on a [0,1] continuous scale for node-node overlap, edge crossing angle, angular resolution, group overlap, and visualization coverage. In addition, we define novel node-and-edge readability metrics to provide more localized identification of where improvement is needed. We describe the trade-offs inherent in optimizing individual metrics as well as recommend metric optimizations for particular tasks. Our metrics are implemented in a JavaScript® API (application programming interface) to make them widely available to designers of web-based visualization tools, who can use metrics to direct users towards poor areas of the drawing. Our prototype system using the API aims to help designers and theorists evaluate and compare their layouts. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1147/JRD.2015.2411412 | IBM Journal of Research and Development |
Field | DocType | Volume |
Visualization,Computer science,Readability,Human–computer interaction,Multimedia | Journal | 59 |
Issue | ISSN | Citations |
2 | 0018-8646 | 6 |
PageRank | References | Authors |
0.42 | 41 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cody Dunne | 1 | 437 | 27.88 |
| Steven I. Ross | 2 | 6 | 0.42 |
| Ben Shneiderman | 3 | 12772 | 3081.95 |
| Mauro Martino | 4 | 6 | 0.42 |