Data-driven Storytelling in Dynamic Graph Comics through Hierarchical Clustering

Abstract

In this work, we propose a tool to generate a dynamic graph comic given dense, time-series edge-vertex data. Prior research has demonstrated the effectiveness of node-link diagrams as an expressive medium for storytelling with dynamic graphs, and in this work, we develop an interface that generates a customizable comic strip consisting of node-link diagram snapshots. We use hierarchical aggregation to cluster and pile graphs based on the number of frames a user may wish to see, with each frame depicting a snapshot in time. We validate the interface with real-world datasets to understand temporal changes in a graph network, and evaluate the interface against an expert audience. Finally, we propose a path forward for improvement of dynamic graph comics as a storytelling medium.