How does this skill improve Claude's coding abilities?

This skill provides Claude with domain-specific patterns for graph data handling, optimized installation workflows, and the correct boilerplate for message-passing layers, reducing errors in complex GNN implementations.

Can I use this skill for molecular property prediction?

Yes, the skill includes patterns for loading molecular datasets like QM9 and designing GNN architectures specifically suited for chemical and biological graph data.

What is PyTorch Geometric (PyG)?

PyTorch Geometric is a library built on top of PyTorch designed specifically for developing and training Graph Neural Networks (GNNs) on irregular data structures like graphs, point clouds, and manifolds.

Does it support large-scale graph learning?

Yes, it covers advanced topics such as neighbor sampling, distributed training, and efficient mini-batching using the PyG DataLoader.

PyTorch Geometric Graph Learning

Name: PyTorch Geometric Graph Learning
Author: K-Dense-AI

byK-Dense-AI

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2,188

数据科学与机器学习

Develops and trains Graph Neural Networks (GNNs) for node classification, link prediction, and geometric deep learning tasks.

关于

This skill empowers Claude with specialized knowledge of the PyTorch Geometric (PyG) library, a premier framework for deep learning on graphs and irregular structures. It provides implementation patterns for popular architectures like GCN, GAT, and GraphSAGE, while offering deep guidance on the message-passing paradigm and custom layer development. Users can leverage this skill to handle complex data structures, manage mini-batching for large-scale graphs, and implement domain-specific solutions for molecular biology, social network analysis, and recommendation systems.

主要功能

2,188 GitHub stars
Integration with benchmark datasets like Planetoid, QM9, and TUDataset
Expert guidance on MessagePassing class for custom layer architectures
Implementation of 40+ pre-built GNN layers including GCN and Attention mechanisms
Structured graph data modeling using COO format and Data objects
Efficient mini-batch processing and global pooling for graph-level tasks

使用场景

Building advanced recommendation systems using citation or knowledge graphs
Analyzing social networks for community detection and influence prediction
Predicting molecular properties for drug discovery and chemical research

关于

主要功能

2,188 GitHub stars
Integration with benchmark datasets like Planetoid, QM9, and TUDataset
Expert guidance on MessagePassing class for custom layer architectures
Implementation of 40+ pre-built GNN layers including GCN and Attention mechanisms
Structured graph data modeling using COO format and Data objects
Efficient mini-batch processing and global pooling for graph-level tasks

使用场景

Building advanced recommendation systems using citation or knowledge graphs
Analyzing social networks for community detection and influence prediction
Predicting molecular properties for drug discovery and chemical research