This work introduces TopoBenchmarkX, a modular open-source library designed
to standardize benchmarking and accelerate research in Topological Deep
Learning (TDL). TopoBenchmarkX maps the TDL pipeline into a sequence of
independent and modular components for data loading 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 TopoBenchmarkX is that
it allows for the transformation and lifting between topological domains. This
enables, for example, to obtain richer data representations and more
fine-grained analyses by mapping the topology and features of a graph to
higher-order topological domains such as simplicial and cell complexes. The
range of applicability of TopoBenchmarkX is demonstrated by benchmarking
several TDL architectures for various tasks and datasets.

TopoBenchmarkX 是一个模块化的开源库，旨在标准化基于拓扑的深度学习的基准测试，并加速研究。它将拓扑深度学习的流程映射为一系列独立和模块化的组件，包括数据加载和处理、模型训练、优化和评估。TopoBenchmarkX 的关键特点是它允许在拓扑领域之间进行转换和提升，以获得更丰富的数据表达和更精细的分析。通过对多个任务和数据集的拓扑深度学习架构进行基准测试，展示了 TopoBenchmarkX 的适用范围。