{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/topobenchmarkx-a-framework-for-benchmarking","title":"TopoBench: A Framework for Benchmarking Topological Deep Learning","arxiv_id":"2406.06642","date":"2024-06-09","proceeding":null,"authors":["Lev Telyatnikov","Guillermo Bernardez","Marco Montagna","Mustafa Hajij","Martin Carrasco","Pavlo Vasylenko","Mathilde Papillon","Ghada Zamzmi","Michael T. Schaub","Jonas Verhellen","Pavel Snopov","Bertran Miquel-Oliver","Manel Gil-Sorribes","Alexis Molina","Victor Guallar","Theodore Long","Julian Suk","Patryk Rygiel","Alexander Nikitin","Giordan Escalona","Michael Banf","Dominik Filipiak","Max Schattauer","Liliya Imasheva","Alvaro Martinez","Halley Fritze","Marissa Masden","Valentina Sánchez","Manuel Lecha","Andrea Cavallo","Claudio Battiloro","Matt Piekenbrock","Mauricio Tec","George Dasoulas","Nina Miolane","Simone Scardapane","Theodore Papamarkou"],"abstract":"This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL into a sequence of independent modules for data generation, loading, transforming and processing, as well as model training, optimization and evaluation. This modular organization provides flexibility for modifications and facilitates the adaptation and optimization of various TDL pipelines. A key feature of TopoBench is its support for transformations and lifting across topological domains. Mapping the topology and features of a graph to higher-order topological domains, such as simplicial and cell complexes, enables richer data representations and more fine-grained analyses. The applicability of TopoBench is demonstrated by benchmarking several TDL architectures across diverse tasks and datasets.","url_abs":"https://arxiv.org/abs/2406.06642v2","url_pdf":"https://arxiv.org/pdf/2406.06642v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"topobenchmarkx-a-framework-for-benchmarking","repo_url":"https://github.com/geometric-intelligence/TopoBench","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"topobenchmarkx-a-framework-for-benchmarking","repo_url":"https://github.com/pyt-team/TopoBenchmarkX","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"topobenchmarkx-a-framework-for-benchmarking","repo_url":"https://github.com/geometric-intelligence/topobenchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.06642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06642"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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