Clustering for Stacked Edge Splatting

Abdelaal, Moataz; Hlawatsch, Marcel; Burch, Michael; Weiskopf, Daniel

Clustering for Stacked Edge Splatting

Files

127-134.pdf (7.17 MB)

stackedparalleledgesplatting-vmv18-fin_supplementary.pdf (7.79 MB)

Date

2018

Authors

Abdelaal, Moataz
Hlawatsch, Marcel
Burch, Michael
Weiskopf, Daniel

Publisher

The Eurographics Association

Abstract

We present a time-scalable approach for visualizing dynamic graphs. By adopting bipartite graph layouts known from parallel edge splatting, individual graphs are horizontally stacked by drawing partial edges, leading to stacked edge splatting. This allows us to uncover the temporal patterns together with achieving time-scalability. To preserve the graph structural information, we introduce the representative graph where edges are aggregated and drawn at full length. The representative graph is then placed on the top of the last graph in the (sub)sequence. This allows us to obtain detailed information about the partial edges by tracing them back to the representative graph. We apply sequential temporal clustering to obtain an overview of different temporal phases of the graph sequence together with the corresponding structure for each phase. We demonstrate the effectiveness of our approach by using real-world datasets.

        @inproceedings{10.2312:vmv.20181262
,
booktitle = {Vision, Modeling and Visualization
},
editor = {Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
},
title = {{Clustering for Stacked Edge Splatting
}},
author = {Abdelaal, Moataz and 
Hlawatsch, Marcel and 
Burch, Michael and 
Weiskopf, Daniel
},
year = {2018
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-072-7
},
DOI = {10.2312/vmv.20181262
}
}