Clustering for Stacked Edge Splatting

We present a time-scalable approach for visualizing dynamic graphs. By adopting bipartite graph layouts known from paralleledge 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 thenplaced on the top of the last graph in the (sub)sequence. This allows us to obtain detailed information about the partial edges bytracing 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 effectivenessof our approach by using real-world datasets