pytorch_geometric

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[![PyPI Version][pypi-image]][pypi-url]
[![PyPI Download][pypi-download-image]][pypi-download-url]
[![Slack][slack-image]][slack-url]
[![Contributing][contributing-image]][contributing-url]

**[Documentation](https://pytorch-geometric.readthedocs.io)** |
**[PyG 1.0 Paper](https://arxiv.org/abs/1903.02428)** |
**[PyG 2.0 Paper](https://arxiv.org/abs/2507.16991)** |
**[Colab Notebooks](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** |
**[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** |
**[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**

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**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/advanced/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.

**[Click here to join our Slack community!][slack-url]**

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  <a href="https://medium.com/stanford-cs224w"><img style="max-width: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a>
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- [Library Highlights](#library-highlights)
- [Quick Tour for New Users](#quick-tour-for-new-users)
- [Architecture Overview](#architecture-overview)
- [Implemented GNN Models](#implemented-gnn-models)
- [Installation](#installation)

## Library Highlights

Whether you are a machine learning researcher or first-time user of machine learning toolkits, here are some reasons to try out PyG for machine learning on graph-structured data.

- **Easy-to-use and unified API**:
  All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a [quick tour](#quick-tour-for-new-users)).
  PyG is *PyTorch-on-the-rocks*: It utilizes a tensor-centric API and keeps design principles close to vanilla PyTorch.
  If you are alr