Group-Level Graph Visualization Taxonomy

TL;DR

This paper introduces a new taxonomy of group-level tasks for graph visualization, addressing the need for standardized evaluation of clustering and grouping techniques in various application fields.

Contribution

It defines and characterizes group-level tasks within graph visualization, enhancing the existing task taxonomy for better evaluation and comparison of recent grouping techniques.

Findings

Proposes a set of group-level tasks for graph visualization evaluation.

Characterizes tasks using multi-level typology of visualization tasks.

Aims to improve task categorization and experimental consistency.

Abstract

Task taxonomies for graph and network visualizations focus on tasks commonly encountered when analyzing graph connectivity and topology. However, in many application fields such as the social sciences (social networks), biology (protein interaction models), software engineering (program call graphs), connectivity and topology information is intertwined with group, clustering, and hierarchical information. Several recent visualization techniques, such as BubbleSets, LineSets and GMap, make explicit use of grouping and clustering, but evaluating such visualization has been difficult due to the lack of standardized group-level tasks. With this in mind, our goal is to define a new set of tasks that assess group-level comprehension. We propose several types of group-level tasks and provide several examples of each type. Finally, we characterize some of the proposed tasks using the multi-level typology of abstract visualization tasks. We believe that adding group-level tasks to the task taxonomy for graph visualization would make the taxonomy more useful for the recent graph visualization techniques. It would help evaluators define and categorize new tasks, and it would help generalize individual results collected in controlled experiments.