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torch-geometric

Build and train graph neural networks with PyTorch Geometric.

1|Updated Mar 19, 2026
Search Tags:#graph-neural-networks#pyg#gnn#node-classification#graph-classification#pytorch-geometric#link-prediction
One-click install
npx skills add https://github.com/JosephWoodall/noosphere --skill torch-geometric-josephwoodall
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torch-geometric
Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/torch-geometric
Command: npx skills add https://github.com/JosephWoodall/noosphere --skill torch-geometric-josephwoodall

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torch_geometric, numpy, matplotlib, networkx, and includes scripts (resource) and references (resource) components.

What problem does it solve?

PyG enables building and training Graph Neural Networks with PyTorch Geometric (PyG) for a wide range of graph ML tasks.

Core Features & Use Cases

  • Node classification, graph classification, and link prediction using state-of-the-art GNN layers (GCN, GAT, GraphSAGE, GIN, etc.)
  • Support for heterogeneous graphs, molecular property prediction, and large-scale graph datasets with ready-to-use templates and example scripts.
  • Use cases across academia and industry for social networks, knowledge graphs, chemistry, and recommendation systems.

Quick Start

Install PyG, load a small dataset, and train a simple GCN model.

Frequently Asked Questions about torch-geometric

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train a graph neural network for node classification using PyTorch?▼

Train a graph neural network for node classification by loading a dataset into PyTorch Geometric and using prebuilt GNN layers like GCN or GAT. Templates and scripts accelerate building and training models.

What graph ML tasks can I solve with PyTorch Geometric?▼

PyTorch Geometric supports graph ML tasks including node classification, graph classification, link prediction, heterogeneous graphs, and molecular property prediction across small to large-scale graphs.

Does this graph neural network template support heterogeneous graphs and molecular property prediction?▼

Yes, it supports heterogeneous graphs and molecular property prediction. It provides ready-to-use templates and example scripts for these tasks alongside node classification and link prediction.

What Python dependencies do I need to run graph neural network scripts with PyG?▼

You need PyTorch, PyTorch Geometric, NumPy, Matplotlib, and NetworkX installed. This standard Python stack supports building, training, and visualizing graph neural networks.

What's the best way to build a GraphSAGE model for link prediction on social networks?▼

Build a GraphSAGE model for link prediction on social networks using PyTorch Geometric's prebuilt state-of-the-art GNN layers. Ready-to-use templates streamline development for social network datasets.

When should I not use PyTorch Geometric for graph classification?▼

Avoid using PyTorch Geometric for graph classification if your environment lacks standard dependencies like PyTorch and NumPy. It is designed for graph ML tasks requiring these specific frameworks.

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