Name
Abstract | ||
|---|---|---|
We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1177/1473871619882019 | INFORMATION VISUALIZATION |
Keywords | DocType | Volume |
Dynamic networks, visual analytics, cluster analysis, data visualization, time-series analysis | Journal | 19 |
Issue | ISSN | Citations |
2 | 1473-8716 | 4 |
PageRank | References | Authors |
0.43 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sugeerth Murugesan | 1 | 12 | 1.88 |
| Kristofer E Bouchard | 2 | 18 | 8.99 |
| Jesse A. Brown | 3 | 121 | 6.31 |
| Mariam Kiran | 4 | 4 | 0.43 |
| Dan Lurie | 5 | 4 | 0.43 |
| Bernd Hamann | 6 | 2283 | 206.78 |
| Gunther H. Weber | 7 | 358 | 26.23 |