IEEE VIS 2024 Content: DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs

Joohee Kim - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Hyunwook Lee - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Duc M. Nguyen - Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of

Minjeong Shin - Australian National University, Canberra, Australia

Bum Chul Kwon - IBM Research, Cambridge, United States

Sungahn Ko - UNIST, Ulsan, Korea, Republic of

Niklas Elmqvist - Aarhus University, Aarhus, Denmark

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Room: Bayshore V

2024-10-17T17:00:00ZGMT-0600Change your timezone on the schedule page
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Exemplar figure, described by caption below
DG Comics offers a Summary View that facilitates the automatic generation of comic templates, sliders for filtering and highlighting nodes, a Graph Comic View for editing the graph comic, and Main Character and Supporting Character tables for managing nodes. It also includes a Timeline View for exploring graph snapshots. Users can switch to the Node Attribute Table to select specific main characters or to the Community View to inspect the evolution of node relationships. The tool supports (M) mental map preservation by fixing nodes across displays and visualizes (O) community changes using bubble sets.
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Keywords

Data-driven storytelling, narrative visualization, dynamic graphs, graph comics

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.