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TopoBench: A Framework for Benchmarking Topological Deep Learning

1 Pith paper cite this work. Polarity classification is still indexing.

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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.

fields

cs.LG 1

years

2025 1

verdicts

REJECT 1

representative citing papers

Heat Kernel Goes Topological

cs.LG · 2025-07-16 · reject · novelty 5.0

TopoHKS defines a weighted combinatorial-complex Laplacian and heat kernel descriptor, claiming maximal expressive power; the supporting uniqueness theorem is incorrect.

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  • Heat Kernel Goes Topological cs.LG · 2025-07-16 · reject · none · ref 42 · internal anchor

    TopoHKS defines a weighted combinatorial-complex Laplacian and heat kernel descriptor, claiming maximal expressive power; the supporting uniqueness theorem is incorrect.