TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernárdez, Montagna, Marco, Mustafa Hajij, Carrasco, Martin, Pavlo Vasylenko, Papillon, Mathilde, Ghada Zamzmi, Michael T. Schaub, Verhellen, Jonas, Snopov, Pavel, Miquel-Oliver, Bertran, Gil-Sorribes, Manel, Molina, Alexis, Guallar, Victor, Long, Theodore, Suk, Julian, Rygiel, Patryk, Nikitin, Alexander, Escalona, Giordan · arXiv (Cornell University) · 2024
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.