PyTorch Geometric

PyTorch Geometric (PyG) is the specialized library for building and training Graph Neural Networks (GNNs) on structured data like graphs, point clouds, and manifolds, all within the PyTorch framework.

PyTorch Geometric (PyG) is a powerful, modular extension for deep learning on irregular data structures (graphs, meshes, point clouds). It provides a comprehensive suite of state-of-the-art Graph Neural Network (GNN) models and a simplified API for rapid development. Key features include the `torch_geometric.data.Data` object for efficient graph representation, a specialized `DataLoader` for mini-batching large and small graphs, and optimized CPU/CUDA kernels for performance. PyG accelerates geometric deep learning: users can implement a GNN model in 10-20 lines of code and leverage multi-GPU support for scalable training on common benchmark datasets (e.g., Cora, QM9).

https://github.com/pyg-team/pytorch_geometric