PyG is the ultimate library for Graph Neural Networks, built upon PyTorch.

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  • PyG is the ultimate library for Graph Neural Networks
    PyG is the ultimate library for Graph Neural Networks
    Build graph learning pipelines with ease.
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What is PyG?

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data. PyG is both friendly to machine learning researchers and first-time users of machine learning toolkits.

Easy-to-use and unified API

Spend less time worrying about the low-level mechanics of implementing and working with Graph Neural Networks. All it takes is 10-20 lines of code to get started training a custom GNN model.


PyG utilizes a tensor-centric API and keeps design principles close to vanilla PyTorch. If you are already familiar with PyTorch, utilizing PyG is straightforward.

Comprehensive and flexible

PyG covers a large number of state-of-the-art GNN architectures and training and scalability procedures, and can easily be extended to fit your specific use-case or conduct your own GNN research.

Powerful API for designing GNNs

The new GraphGym API in PyG can help users easily reproduce GNN experiments, launching and analyzing thousands of GNN experiments, and registering customized modules to the GNN learning pipeline.

The core PyG team

Matthias Fey

Core lead

Jiaxuan You

Core contributor

Rex Ying

Core contributor

Guohao Li

Core contributor

Jinu Sunil

Core contributor

Jan Eric Lenssen

Core contributor

Ivaylo Bahtchevanov

Community lead

Jure Leskovec