Name
Abstract | ||
|---|---|---|
The visualization of dynamic graphs demands visually encoding at least three major data dimensions: vertices, edges, and time steps. Many of the state-of-the-art techniques can show an overview of vertices and edges but lack a data-scalable visual representation of the time aspect. In this paper, we address the problem of displaying dynamic graphs with a thousand or more time steps. Our proposed interleaved parallel edge splatting technique uses a time-to-space mapping and shows the complete dynamic graph in a static visualization. It provides an overview of all data dimensions, allowing for visually detecting time-varying data patterns; hence, it serves as a starting point for further data exploration. By applying clustering and ordering techniques on the vertices, edge splatting on the links, and a dense time-to-space mapping, our approach becomes visually scalable in all three dynamic graph data dimensions. We illustrate the usefulness of our technique by applying it to call graphs and US domestic flight data with several hundred vertices, several thousand edges, and more than a thousand time steps. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1111/cgf.13185 | Comput. Graph. Forum |
Field | DocType | Volume |
Graph,Computer science,Theoretical computer science | Journal | 36 |
Issue | ISSN | Citations |
3 | 0167-7055 | 4 |
PageRank | References | Authors |
0.40 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Burch | 1 | 854 | 66.47 |
| Marcel Hlawatsch | 2 | 128 | 9.80 |
| Daniel Weiskopf | 3 | 2988 | 204.30 |