mlsanityData to 7 Oct 2026

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains

arXiv 2402.02441 ↗First posted 4 Feb 2024cs.LG cs.AI cs.MScited by 22

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Abstract (arXiv)

We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelX is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at https://pyt-team.github.io/}{https://pyt-team.github.io/.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 4 Feb 2024.

Builds on

17 of 25 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  6. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  7. 2018Hypergraph Neural Networkscited by 482
  8. 2019Topological Signal Processing over Simplicial Complexescited by 94
  9. 2022Topological Deep Learning: Going Beyond Graph Datacited by 92
  10. 2018Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplaciancited by 84
  11. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 81
  12. 2021Principled Simplicial Neural Networks for Trajectory Predictioncited by 77
5 more
  1. 2020Cell Complex Neural Networkscited by 66
  2. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  3. 2022Cell Attention Networkscited by 34
  4. 2023Convolutional Learning on Simplicial Complexescited by 26
  5. 2023ICML 2023 Topological Deep Learning Challenge : Design and Resultscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 7; 2026: 5.

22 papers.

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  1. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  2. 2024TopoBench: A Framework for Benchmarking Topological Deep Learningcited by 24
  3. 2024E(n) Equivariant Topological Neural Networkscited by 22
  4. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  5. 2024TopoTune : A Framework for Generalized Combinatorial Complex Neural Networkscited by 18
  6. 2024MANTRA: The Manifold Triangulations Assemblagecited by 12
  7. 2024Attending to Topological Spaces: The Cellular Transformercited by 11
  8. 2025DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learningcited by 5
  9. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  10. 2024ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domaincited by 3
  11. 2025CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learningcited by 3
  12. 2025Continuous Simplicial Neural Networkscited by 2
10 more
  1. 2025HLSAD: Hodge Laplacian-based Simplicial Anomaly Detectioncited by 2
  2. 2025Topotein: Topological Deep Learning for Protein Representation Learningcited by 2
  3. 2026Topology-Preserving Neural Operator Learning via Hodge Decompositioncited by 1
  4. 2024Learning From Simplicial Data Based on Random Walks and 1D Convolutionscited by 0
  5. 2025Enhancing the Utility of Higher-Order Information in Relational Learningcited by 0
  6. 2025Directed Semi-Simplicial Learning with Applications to Brain Activity Decodingcited by 0
  7. 2026TopoU-Net: a U-Net architecture for topological domainscited by 0
  8. 2026Scaling Higher-Order Graph Learning with Maximal Clique Complexescited by 0
  9. 2026Differentiable Lifting for Topological Neural Networkscited by 0
  10. 2026TopoEmbedX: A General Framework for Representation Learning on Topological Domainscited by 0