GLaDOS: Graph Layout algorithm Datasets for Open Science

Main Article Content

Sara Di Bartolomeo
Connor Wilson
https://orcid.org/0000-0002-6936-4078
Eduardo Puerta
Tarik Crnovrsanin
https://orcid.org/0000-0002-4397-5532
Alexander Frings
https://orcid.org/0009-0009-1718-2967
Cody Dunne
https://orcid.org/0000-0002-1609-9776

Abstract


Introduction


Computational evaluations are crucial for standardized and objective evaluation of graph layout algorithms. Standard benchmark datasets facilitate comparison with prior work, and reliable access to datasets is fundamental for replicability. However, there is no comprehensive repository of benchmark datasets for Graph Drawing, and many datasets have been lost.


Data collection


We collected 196 papers from Graph Drawing, IEEE venues, and Eurographics venues that include computational evaluations of graph layout algorithms. We searched for the datasets used.


Data analysis


We archived datasets we found and re-created ones that were lost but had sufficient replication instructions. We classified graphs by their features and statistics. We also found text and images from papers using those graphs.




Implementation


We implemented graph creation, data analysis, and website code.




Materials


We provide a Graph Layout Benchmark Datasets website and a long-term archive which includes the documentation, code, and data for the website, benchmark datasets, analysis code, graph conversion code, and this paper.




Conclusion


We provide a resource for the Graph Drawing and visualization communities to use to find datasets for computational evaluations of graph layout algorithms. Our organization by features and statistics supports rapid identification of relevant graphs. We have re-created and archived graphs used in research for their long-term preservation.


Article Details

How to Cite
[1]
Di Bartolomeo, S. et al. 2026. GLaDOS: Graph Layout algorithm Datasets for Open Science. Journal of Visualization and Interaction. 1, 1 (Jan. 2026). DOI:https://doi.org/10.54337/jovi.v1i1.11236.
Section
Articles

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