pytorch_geometric and deepsnap
PyTorch Geometric provides the core GNN primitives and layers, while DeepSnap builds on top of it as a higher-level library for converting graph data formats and integrating with PyTorch Geometric, making them complements rather than competitors.
About pytorch_geometric
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
Provides a message-passing API for implementing custom GNN layers and pre-built convolution operators (GCNConv, EdgeConv, etc.) that handle node aggregation and feature propagation. Supports heterogeneous graphs, dynamic temporal graphs, and large-scale models with millions of nodes, alongside specialized data loaders for mini-batch training on both small and giant graphs. Includes built-in benchmark datasets and graph transforms for point clouds and 3D meshes, with `torch.compile` and multi-GPU support for production deployments.
About deepsnap
snap-stanford/deepsnap
Python library assists deep learning on graphs
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