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.
TopoBench: A Framework for Benchmarking Topological Deep Learning
TopoBenchmarkX provides a modular framework for benchmarking and researching Topological Deep Learning, enabling efficient transformation and lifting between topological domains.
- Year
- 2024
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- arXiv 2024
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- 37
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- arxiv.org/abs/2406.06642v2ARXIV-DEFAULT
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37Simone ScardapaneLev TelyatnikovGuillermo BernardezMarco MontagnaMustafa HajijMartin CarrascoPavlo VasylenkoMathilde PapillonGhada ZamzmiMichael T. SchaubJonas VerhellenPavel SnopovBertran Miquel-OliverManel Gil-SorribesAlexis MolinaVictor GuallarTheodore LongJulian SukPatryk RygielAlexander NikitinGiordan EscalonaMichael BanfDominik FilipiakMax SchattauerLiliya ImashevaAlvaro MartinezHalley FritzeMarissa MasdenValentina SánchezManuel LechaAndrea CavalloClaudio BattiloroMatt PiekenbrockMauricio TecGeorge DasoulasNina MiolaneTheodore Papamarkou