Journal Paper

@article{kim2025DGComics, title={DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs}, author={Joohee Kim and Hyunwook Lee and Nguyen, Duc M. and Minjeong Shin and Kwon, Bum Chul and Sungahn Ko and Niklas Elmqvist}, url={https://doi.org/10.1109/TVCG.2024.3456340}, year={2025}, date={2025-01-01}, journal={IEEE Transactions on Visualization and Computer Graphics}, publisher={IEEE Computer Society}, address={Los Alamitos, CA, USA}, volume={31}, number={1}, pages={973--983}, doi={10.1109/TVCG.2024.3456340}, issn={1077-2626}, abstract={Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs - graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG COMICS, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.}, language={English}, keywords={data-driven storytelling,dynamic graphs,graph comics,narrative visualization}, }