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PyG Documentation — pytorch_geometric documentation PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
Home - PyG 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
pyg-team pytorch_geometric - GitHub PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
Installation — pytorch_geometric documentation For earlier PyTorch versions (torch<=2 5 0), you can install PyG via Anaconda for all major OS, and CUDA combinations If you have not yet installed PyTorch, install it via conda install as described in its official documentation
Community - PyG GraphGym is a platform for designing and evaluating Graph Neural Networks (GNNs), as originally proposed in the “Design Space for Graph Neural Networks” paper We now officially support GraphGym as part of of PyG
Explaining Graph Neural Networks — pytorch_geometric documentation PyG (2 3 and beyond) provides the torch_geometric explain package for first-class GNN explainability support that currently includes a flexible interface to generate a variety of explanations via the Explainer class, several underlying explanation algorithms including, e g , GNNExplainer, PGExplainer and CaptumExplainer,
Releases · pyg-team pytorch_geometric - GitHub PyG 2 3 is fully compatible with the next generation release of PyTorch, bringing many new innovations and features such as torch compile() and Python 3 11 support to PyG out-of-the-box
PyG - GitHub Graph Neural Network Library for PyTorch PyG has 5 repositories available Follow their code on GitHub
Introduction by Example — pytorch_geometric documentation We shortly introduce the fundamental concepts of PyG through self-contained examples For an introduction to Graph Machine Learning, we refer the interested reader to the Stanford CS224W: Machine Learning with Graphs lectures