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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 - GitHub Graph Neural Network Library for PyTorch PyG has 5 repositories available Follow their code on GitHub
PyG 2. 0: Scalable Learning on Real World Graphs In this paper, we present Pyg 2 0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities
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
torch-geometric · PyPI 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
PyG 2. 0 Release With this, we are releasing PyG 2 0, a new major release that brings sophisticated heterogeneous graph support, GraphGym and many other exciting features to PyG We finally provide full heterogeneous graph support in PyG 2 0 See here for the accompanying tutorial
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
PyG - NVIDIA NGC 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
Introduction by Example — pytorch_geometric documentation After learning about data handling, datasets, loader and transforms in PyG, it’s time to implement our first graph neural network! We will use a simple GCN layer and replicate the experiments on the Cora citation dataset