Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities

Henry Ehlers, Diana Marin, Hsiang-Yun Wu, Renata Raidou

2024

Abstract

Compound graphs are common across domains, from social science to biochemical pathway studies, and their visualization is important to both their exploration and analysis. However, effectively visualizing a compound graph’s topology and group structure requires careful consideration, as evident by the many different approaches to this particular problem. To better understand the current advancements in compound graph visualization, we have consolidated and streamlined existing surveys’ taxonomies. More specifically, we aim to disentangle the visual relationship between graph topology and group structure from the visual encoding used to visualize its group structure in order to identify interesting gaps in the literature. In so doing, we are able to enumerate a number of lessons learned and gain a better understanding of the outstanding research opportunities and practical implications across domains.

Paper Citation

in Harvard Style

Ehlers H., Marin D., Wu H. and Raidou R. (2024). Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP; ISBN 978-989-758-679-8, SciTePress, pages 697-708. DOI: 10.5220/0012431200003660

in Bibtex Style

@conference{ivapp24,
author={Henry Ehlers and Diana Marin and Hsiang-Yun Wu and Renata Raidou},
title={Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP},
year={2024},
pages={697-708},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012431200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP
TI - Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities
SN - 978-989-758-679-8
AU - Ehlers H.
AU - Marin D.
AU - Wu H.
AU - Raidou R.
PY - 2024
SP - 697
EP - 708
DO - 10.5220/0012431200003660
PB - SciTePress