torch-geometric

原文涵盖的主题

• PyTorch Geometric (PyG)
• Overview
• When to Use This Skill
• Quick Start
• Installation
• Basic Graph Creation
• Loading a Benchmark Dataset
• Core Concepts

PyTorch Geometric is a library built on PyTorch for developing and training Graph Neural Networks (GNNs). Apply this skill for deep learning on graphs and irregular structures, including mini-batch processing, multi-GPU training, and geometric deep learning applications. This skill should be used when working with: Graph-based machine learning: Node classification, graph classification, link prediction Molecular property prediction: Drug discovery, chemical property prediction Social network analysis: Community detection, influence prediction Citation networks: Paper classification, recommendation systems 3D geometric data: Point clouds, meshes, molecular structures Heterogeneous graphs: Multi-type nodes and edges (e.g., knowledge graphs) Large-scale graph learning: Neighbor sampling, distributed training For additional dependencies (sparse operations, clustering): PyG represents graphs using the torchgeometric.data.Data class with these key attributes: data.x: Node feature matrix [numnodes, numnodefeatures] data.edgeindex: Graph connectivity in COO format [2, numedges] data.edgeattr: Edge feature matrix [numedges, numedgefeatures] (optional) data.y: Target labels for nodes or grap

这里只展示节选，请以完整原文和当前要求为准。