Visualization Tasks for Unlabelled Graphs
This work addresses a foundational gap in visualization research for unlabelled graphs, which is incremental as it builds on existing frameworks to improve evaluation methods.
The paper tackles the problem of understanding tasks for unlabelled graphs by proposing a data abstraction model and a taxonomy based on Scope, Action, and Target, and demonstrates its evaluative power through a preliminary assessment of 6 visualizations for each task, considering effort and success likelihood across graph scales.
We investigate tasks that can be accomplished with unlabelled graphs, which are graphs with nodes that do not have attached persistent or semantically meaningful labels. New visualization techniques to represent unlabelled graphs have been proposed, but more understanding of unlabelled graph tasks is required before these techniques can be adequately evaluated. Some tasks apply to both labelled and unlabelled graphs, but many do not translate between these contexts. We propose a data abstraction model that distinguishes the Unlabelled context from the increasingly semantically rich Labelled, Attributed, and Augmented contexts. We filter tasks collected and gleaned from the literature according to our data abstraction and analyze the surfaced tasks, leading to a taxonomy of abstract tasks for unlabelled graphs. Our task taxonomy is organized according to the Scope of the data at play, the Action intended by the user, and the Target data under consideration. We show the descriptive power of this task abstraction by connecting to concrete examples from previous frameworks, and connect these abstractions to real-world problems. To showcase the evaluative power of the taxonomy, we perform a preliminary assessment of 6 visualizations for each task. For each combination of task and visual encoding, we consider the effort required from viewers, the likelihood of task success, and how both factors vary between small-scale and large-scale graphs.