PyTorch Geometric (PyG)

About

PyTorch Geometric (PyG) is a powerful library built on PyTorch that simplifies the implementation of Graph Neural Networks. This skill enables Claude to assist with geometric deep learning tasks, including node and graph classification, link prediction, and heterogeneous graph processing. It provides standardized patterns for implementing state-of-the-art architectures like GCN, GAT, and GraphSAGE, while offering best practices for efficient mini-batching, large-scale graph sampling, and custom message-passing layer development.

Key Features

• Custom message-passing interface for designing unique GNN architectures
• 8 GitHub stars
• Efficient graph data handling using COO format and the Data/HeteroData classes
• Extensive support for molecular graphs, social networks, and 3D geometric data
• Implementation of 40+ convolutional layers including GAT, GCN, and GraphSAGE
• Optimized mini-batching through block-diagonal adjacency matrix concatenation

Use Cases

• Analyzing irregular structures like 3D point clouds and citation networks
• Predicting molecular properties and chemical interactions for drug discovery
• Building recommendation systems and community detection models for social platforms