Pith. sign in

REVIEW 3 cited by

Group-Level Graph Visualization Taxonomy

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1403.7421 v1 pith:B7JZKP3R submitted 2014-03-21 cs.HC

classification cs.HC
keywords tasksvisualizationgraphgroup-levelseveraltaxonomyclusteringconnectivity
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 23 citations worldwide. Full citation record

  1. SNAP-tFDP: Massively Scalable Graph Layouts via Sparse Negative Sampling

    cs.GR 2026-08 conditional novelty 6.0 of 10

    SNAP-tFDP approximates degree-weighted repulsion by sampling k negative nodes per edge, giving O(|E|) time and strong cluster separation on large graphs.

  2. A Design Study on Voice-based Interaction for Immersive Network Visualization and Analysis

    cs.HC 2026-07 conditional novelty 6.0 of 10

    Voice-primary, LLM-driven command input can make immersive network visualization feel more usable and less cognitively demanding than controller-based interaction, according to a qualitative user study.

  3. A Quality Metric for Visualization of Clusters in Graphs

    cs.DS 2019-08 conditional novelty 5.0 of 10

    A new metric scores graph drawings by comparing ground-truth clusters to k-means clusters computed from drawing coordinates.

Pith tools