mlsanityData to 7 Oct 2026

TopoBench: A Framework for Benchmarking Topological Deep Learning

arXiv 2406.06642 ↗First posted 9 Jun 2024cs.LGcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 45 of its 72 references that are in the map. First posted on arXiv 9 Jun 2024.

Builds on

45 of 72 references.

References · 45

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  7. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
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  11. 2020Benchmarking Graph Neural Networkscited by 672
  12. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
33 more
  1. 2018Hypergraph Neural Networkscited by 482
  2. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  3. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  4. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  5. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  6. 2021UniGNN: a Unified Framework for Graph and Hypergraph Neural Networkscited by 127
  7. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124
  8. 2020Simplicial Neural Networkscited by 105
  9. 2020HNHN: Hypergraph Networks with Hyperedge Neuronscited by 104
  10. 2019Topological Signal Processing over Simplicial Complexescited by 94
  11. 2022Topological Deep Learning: Going Beyond Graph Datacited by 92
  12. 2020Differentiable Graph Module (DGM) for Graph Convolutional Networkscited by 86
  13. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 81
  14. 2021Principled Simplicial Neural Networks for Trajectory Predictioncited by 77
  15. 2020Cell Complex Neural Networkscited by 66
  16. 2021What are higher-order networks?cited by 66
  17. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  18. 2021The physics of higher-order interactions in complex systemscited by 63
  19. 2020giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Explorationcited by 55
  20. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  21. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  22. 2022Cell Attention Networkscited by 34
  23. 2023Generalized Simplicial Attention Neural Networkscited by 27
  24. 2023Convolutional Learning on Simplicial Complexescited by 26
  25. 2024TopoX: A Suite of Python Packages for Machine Learning on Topological Domainscited by 22
  26. 2024E(n) Equivariant Topological Neural Networkscited by 22
  27. 2021BScNets: Block Simplicial Complex Neural Networkscited by 21
  28. 2022Simplicial Attention Neural Networkscited by 21
  29. 2023From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Modulecited by 19
  30. 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphscited by 15
  31. 2023Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Designcited by 13
  32. 2022EGG-GAE: scalable graph neural networks for tabular data imputationcited by 7
  33. 2023Topo-MLP : A Simplicial Network Without Message Passingcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 10.

24 papers.

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  1. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  2. 2024TopoTune : A Framework for Generalized Combinatorial Complex Neural Networkscited by 18
  3. 2024MANTRA: The Manifold Triangulations Assemblagecited by 12
  4. 2025HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representationscited by 5
  5. 2025DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learningcited by 5
  6. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  7. 2024ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domaincited by 3
  8. 2025Topotein: Topological Deep Learning for Protein Representation Learningcited by 2
  9. 2026Have Graph -- Will Lift? The Case for Higher-Order Benchmarkscited by 2
  10. 2025Heat Kernel Goes Topologicalcited by 1
  11. 2025GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalizationcited by 1
  12. 2025Enhancing the Utility of Higher-Order Information in Relational Learningcited by 0
12 more
  1. 2025Directed Semi-Simplicial Learning with Applications to Brain Activity Decodingcited by 0
  2. 2025A Remedy for Over-Squashing in Graph Learning via Forman-Ricci Curvature based Graph-to-Hypergraph Structural Liftingcited by 0
  3. 2025The Human Brain as a Combinatorial Complexcited by 0
  4. 2026CCMamba: Topologically-Informed Selective State-Space Networks on Combinatorial Complexes for Higher-Order Graph Learningcited by 0
  5. 2026TopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repaircited by 0
  6. 2026Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networkscited by 0
  7. 2026No Triangulation Without Representation: Generalization in Topological Deep Learningcited by 0
  8. 2026OgBench: A Framework for Evaluating Graph Neural Networks on Omics Datacited by 0
  9. 2026Collapsed Effective Operators for Higher-order Structurescited by 0
  10. 2026Do Sheaf Neural Networks Use Holonomy? A Measure--Intervene--Control Studycited by 0
  11. 2026Differentiable Lifting for Topological Neural Networkscited by 0
  12. 2026Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learningcited by 0