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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
Venue
arXiv 2024
Authors
37
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Abstract onlyARXIV-DEFAULT

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arxiv.org/abs/2406.06642v2ARXIV-DEFAULT
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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.

Authors

37