Interactive Sankey diagrams

By P. Riehmann, M. Hanfler, B. Fröhlich. IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., 2005

424 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e75e623595210bbd5b91dd097e07ca2439e21fec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Weiwei Cui
3. Huamin Qu
4. Yangqiu Song
5. Jason Cory Brunson
6. Xin Tong
7. Mengchen Liu
8. Yingcai Wu

In Signal Processing

1. Jason Cory Brunson
2. Xin Tong
3. Weiwei Cui
4. Huamin Qu
5. Shixia Liu

Citations in the corpus

• ggalluvial: Layered Grammar for Alluvial Plots (2020, methodology): “A widely-used subtype for longitudinal categorical data represent categories as nodes threaded by edges that represent the trajectories and magnitudes of cases (Riehmann, Hanfler, & Froehlich, 2005).”
• ConsensUs (2018, methodology): “Aseniero et al. (2015) created a hybrid visualization combining Sankey diagrams and parallel coordinates in a tree view to support decision making for software release planning (Riehmann et al. 2005).”
• TextFlow: Towards Better Understanding of Evolving Topics in Text (2011, background): “To help users analyze, communicate, and plan the energy distribution in a city, Riehmann et al. [19] develop an interactive Sankey diagram.”