pytorch_geometric and graphein
PyTorch Geometric provides the foundational GNN framework and operations, while Graphein builds specialized protein structure graphs on top of it, making them complements that are often used together in computational biology workflows.
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 graphein
a-r-j/graphein
Protein Graph Library
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