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.

Reference

Ehlers, H., Marin, D., Wu, H.-Y., & 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, HUCAPP and IVAPP (pp. 697–708). https://doi.org/10.5220/0012431200003660