pyg-team/pytorch_geometric

Health v2 · maintenance only

Adoption not part of the score

24032 stars · 4039 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PyG (PyTorch Geometric) is a Python library built on PyTorch for writing and training Graph Neural Networks on structured and irregular data. It provides GNN layers from published research, mini-batch loaders, benchmark datasets, and transforms for graphs, 3D meshes, and point clouds.

Use cases

• train graph neural networks for node classification
• build GNN models for link prediction
• run deep learning on 3D meshes and point clouds
• load and mini-batch many small graphs or one giant graph
• experiment with graph convolutional network architectures
• benchmark GNN models on standard graph datasets

When to choose

• you use PyTorch and need graph deep learning with a tensor-centric API
• you want many pre-implemented GNN models from research papers
• you need multi-GPU training and torch.compile support for GNNs
• you work with irregular structures like graphs, meshes, or point clouds

When to avoid

• your project is built on TensorFlow or JAX rather than PyTorch
• you only need classical graph algorithms without deep learning
• you need a no-dependency lightweight graph library

Facets

library · maturity stable

deep-learning machine-learning data-science machine-learning deep-learning python cross-platform graph-neural-networks geometric-deep-learning pytorch gnn graph-convolutional-networks point-clouds 3d-meshes algorithms gpu

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