torch-geometric-graph-neural-networks
PyTorch Geometric (PyG) — Graph Neural Networks
Overview
PyTorch Geometric is a library built on PyTorch for developing and training Graph Neural Networks (GNNs). It provides 40+ convolutional layers, mini-batch processing via block-diagonal adjacency matrices, neighbor sampling for large-scale graphs, and heterogeneous graph support for multi-type node/edge networks.
When to Use
- Node classification on citation, social, or biological networks
- Graph-level classification (molecular activity, protein function)
- Link prediction (knowledge graphs, recommendation systems)
- Molecular property prediction (drug discovery, quantum chemistry)
- 3D point cloud processing and mesh analysis
- Large-scale graph learning with neighbor sampling (>100K nodes)
- Heterogeneous graphs with multiple node/edge types
- For non-graph deep learning → use PyTorch directly
- For traditional graph algorithms (shortest path, centrality) → use NetworkX
Prerequisites
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