|
- 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
|
|
|