Is it suitable for molecular modeling?

Yes, PyG is widely used in bioinformatics and chemistry. This skill includes patterns for loading molecular datasets and predicting chemical properties.

Can this skill help with large graph datasets?

Yes, the skill includes implementation patterns for mini-batching, neighbor sampling, and distributed training to handle graphs that exceed memory limits.

Does it support custom GNN architectures?

Absolutely. It provides specific guidance on inheriting from the MessagePassing class to create custom aggregation, message, and update functions.

What is PyTorch Geometric (PyG) used for?

PyG is a library built on PyTorch for developing Graph Neural Networks (GNNs) to analyze data structured as graphs, such as social networks, molecules, and citation maps.

PyTorch Geometric (PyG) for Claude

Name: PyTorch Geometric (PyG) for Claude
Author: Microck

byMicrock

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Ciencia de Datos y ML

Implements high-performance Graph Neural Networks and geometric deep learning architectures using the PyTorch Geometric library.

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PyTorch Geometric (PyG) is a specialized Claude Code skill designed to streamline the development of Graph Neural Networks (GNNs). It provides Claude with the domain-specific knowledge required to handle irregular data structures like social networks, molecular graphs, and 3D point clouds. This skill enables efficient implementation of node and graph classification, link prediction, and heterogeneous graph processing. It includes optimized patterns for mini-batching, multi-GPU training, and custom message-passing layers, making it an essential tool for developers working on complex relational data and geometric deep learning applications.

Características Principales

Templates for custom message-passing and neighborhood aggregation layers
Heterogeneous graph support for complex multi-type node and edge relationships
81 GitHub stars
Support for 40+ convolutional layers including GCN, GAT, and GraphSAGE
Efficient mini-batch processing for large-scale graph datasets
Comprehensive integration with benchmark datasets like Cora, QM9, and Planetoid

Casos de Uso

Molecular property prediction for drug discovery and chemical research
3D geometric data processing for point clouds and mesh analysis
Social network analysis for community detection and influence prediction

Acerca de

Características Principales

Templates for custom message-passing and neighborhood aggregation layers
Heterogeneous graph support for complex multi-type node and edge relationships
81 GitHub stars
Support for 40+ convolutional layers including GCN, GAT, and GraphSAGE
Efficient mini-batch processing for large-scale graph datasets
Comprehensive integration with benchmark datasets like Cora, QM9, and Planetoid

Casos de Uso

Molecular property prediction for drug discovery and chemical research
3D geometric data processing for point clouds and mesh analysis
Social network analysis for community detection and influence prediction