TopoBench: A Framework for Benchmarking Topological Deep Learning

TL;DR

TopoBench is an open-source framework that standardizes benchmarking in topological deep learning, enabling flexible, modular analysis of TDL models across various topological domains and datasets.

Contribution

It introduces a modular, extensible library for benchmarking topological deep learning, supporting transformations across topological domains and facilitating research acceleration.

Findings

Benchmarking several TDL architectures across diverse tasks.

Support for transformations and lifting across topological domains.

Enhanced data representations through higher-order topological mapping.

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.