HyperTorch: A Python library for
hypergraph learning and benchmarking

Documentation | Getting started | Tutorials | Contributing

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About the project

HyperTorch is a library for hypergraph learning and benchmarking. It provides standardized workflows for loading hypergraph datasets, training models, evaluating them under comparable settings, and reporting results for both hyperlink prediction and node classification.

The library is built around extensibility: datasets are represented in HIF format and converted into typed tensor objects, models can be implemented as standard Lightning modules, and benchmarking is handled through reusable trainers, samplers, metrics, loggers, and result exporters (Markdown/LaTeX). HyperTorch includes preloaded datasets, mini-batch and full-hypergraph data loading, negative sampling utilities, structural feature enrichers, neural components, and many built-in models.

Use HyperTorch to:

• Benchmark existing models across a shared collection of hypergraph datasets.
• Develop custom PyTorch or Lightning models and compare them with built-in baselines.
• Load local or remote .json and .json.zst HIF datasets and run the same training, evaluation, and reporting pipeline on them.

Table of contents

• Main features
• Getting started
  • Installation
  • Source installation
  • TensorBoard support
  • Run examples
• Contributing
• Documentation
• License
• Support
• Cite

Main features

Getting started

Installation

HyperTorch requires Python 3.10 or newer. The currently documented and CI-tested range is Python 3.10 through 3.14 on Linux x86_64 and ARM/aarch64, macOS arm64, and Windows x64.

For a CPU installation, follow the platform-specific installation guide to install compatible PyTorch and PyG wheels, then install HyperTorch from PyPI:

uv pip install hypertorch

If you use pip, replace uv pip install with pip install. For CUDA or other hardware, you can install the matching PyTorch and PyG wheels within HyperTorch's declared dependency ranges before installing HyperTorch.

Source installation

git clone https://github.com/hypernetwork-research-group/hypertorch.git
cd hypertorch

make setup

See the installation guide for platform notes and dependency ranges.

TensorBoard support

Install the optional TensorBoard integration from PyPI with:

uv pip install "hypertorch[tensorboard]"

For a source installation, use:

make setup-tensorboard

Run examples

Run examples from the repository root with make run. For example:

# Hyperlink prediction
make run examples/hyperlink_prediction/nhp.py

# Node classification
make run examples/node_classification/hypergcn.py

The tutorials guide lists examples for dataset loading, feature enrichment, hyperlink prediction, node classification, sampling, splitting, and training customization.

Contributing

See CONTRIBUTING.md for the contributor quickstart and the development guide for the complete workflow.

Documentation

Read the documentation for installation, tutorials, user guides, API references, development guidance, and release notes.

Build or serve it locally with the documented Makefile targets:

make docs-build
make docs-serve

Use make docs to build and serve in one command. The local site is available at http://127.0.0.1:8000.

License

This project is released under the Apache License 2.0 license. See LICENSE.

Support

• Use GitHub Discussions for questions and ideas.
• Use the GitHub issue tracker for bugs and feature requests.
• Use Discord for community chat.

Please follow SECURITY.md instead of opening a public issue for suspected security vulnerabilities.

Cite

Please cite us if you use HyperTorch in your work:

@software{Citro_HyperTorch_2026,
    author = {Citro, Tiziano and De Vinco, Daniele and Spagnuolo, Carmine},
    title = {{HyperTorch: A Python Library for Hypergraph Learning and Benchmarking}},
    url = {https://github.com/hypernetwork-research-group/hypertorch},
    year = {2026}
}