Topological Deep Learning Challenge 2025: Expanding the Data Landscape
1st Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS 2025)
In short The 2025 Topological Deep Learning Challenge asked for new datasets, benchmark tasks, and data pipelines in TopoBench, and drew 44 qualifying submissions.
Abstract
This paper describes the 2025 edition of the Topological Deep Learning Challenge: Expanding the Data Landscape, hosted at the first Topology, Algebra, and Geometry in Data Science (TAG-DS) Conference. This year’s challenge aimed to address the data bottleneck in the field by systematically expanding the ecosystem of Topological Deep Learning (TDL). Powered by TopoBench, the challenge was organized into two primary missions: enriching the data landscape with diverse datasets, and advancing core data infrastructure. In particular, participants were invited to contribute to the open-source platform by implementing new dataset loaders, designing new benchmark tasks, or engineering robust, scalable data pipelines. The initiative successfully yielded 44 qualifying submissions. This paper outlines the scope of the competition and summarizes the key results and findings, highlighting the new resources now available to the TDL community.
I contributed a TopoBench loader for MoleculeNet (pull request #224) in Category A.1, the category for established graph and point cloud datasets.
Cite
@inproceedings{pmlr-v321-bernardez26a,
title = {Topological Deep Learning Challenge 2025: Expanding the Data Landscape},
author = {Bern\'ardez, Guillermo and Telyatnikov, Lev and Papillon, Mathilde and Montagna, Marco and Theiler, Raffael and Cornelis, Louisa and Mathe, Johan and Ferriol, Miquel and Vasylenko, Pavlo and Van Looy, Jan-Willem and Testa, Lucia and Neri, Bruno and Genovese, Donatella and Weber, Melanie and Wei, Amaury and Devoto, Alessio and Weers, Alexander and Jankowski, Robert and Cino, Loris and Leko, David and Banf, Michael and M\"{u}ller, Jonas and Grapentin, Thomas and Paik, Taejin and Dutta, Abhijeet and Walter, Hugo and Fontanari, Thomas Vaitses and Ghasemi, Ali and Loi, Dario and S\'aez de Oc\'ariz Borde, Haitz and Aguilar-Arg\"uello, Gabriela and da Rosa, Giovanni B. and Saulus, Th\'eo and Dolores-Cuenca, Eric Rubiel and Di Nino, Leonardo and Leroy, Pierrick and Pandolfo, Mario Edoardo and Cavallo, Andrea and Qin, Yu and Snopov, Pavel and Akbari, Amirreza and Meza-Ch\'avez, Ixchel and Van Langendonck, Louis and Able, Jared and Meshcheryakova, Maria Yuffa and Tsay, Henry and Beni\'c, Luka and Filipiak, Dominik and Liu, Patrick and Liang, Huidong and Santos da Rosa Jr., Alexsandro and Cattai, Tiziana and Borges, Henrique M. and Grimaldi, Enrico and Lecha, Manuel and Battiloro, Claudio and Liu, Xuan-Chen and Deshpande, Raj and Johnson, Graham and Morgunov, Igor and Micheron, Hugo and Devaux, R\'emi and Jardin, Antoine and Emerson, Tegan and Fink, Olga and Miolane, Nina},
booktitle = {Proceedings of the 1st Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS 2025)},
pages = {4--14},
year = {2026},
volume = {321},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v321/bernardez26a.html}
}