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

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: March 2024
• ISBN: 978-989-758-679-8
• Location: Rom
• Lecturer: Henry Ehlers
• Event: 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
• DOI: 10.5220/0012431200003660
• Booktitle: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1, HUCAPP and IVAPP
• Pages: 12
• Conference date: 27. February 2024 – 29. February 2024
• Pages: 697 – 708
• Keywords: compound graph visualization, literature survey, group structure visualization

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.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.5220/0012431200003660

BibTeX

@inproceedings{ehlers-2024-vgs,
  title =      "Visualizing Group Structure in Compound Graphs: The Current
               State, Lessons Learned, and Outstanding Opportunities",
  author =     "Henry Ehlers and Diana Marin and Hsiang-Yun Wu and Renata
               Raidou",
  year =       "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.",
  month =      mar,
  isbn =       "978-989-758-679-8",
  location =   "Rom",
  event =      "19th International Joint Conference on Computer Vision,
               Imaging and Computer Graphics Theory and Applications",
  doi =        "10.5220/0012431200003660",
  booktitle =  "Proceedings of the 19th International Joint Conference on
               Computer Vision, Imaging and Computer Graphics Theory and
               Applications - Volume 1, HUCAPP and IVAPP",
  pages =      "12",
  pages =      "697--708",
  keywords =   "compound graph visualization, literature survey, group
               structure visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-vgs/",
}