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torch-geometric

Build and evaluate Graph Neural Networks with PyTorch Geometric templates.

15|2|Updated Dec 17, 2025
Search Tags:#template#training#graph-neural-networks#gnn#pytorch-geometric#graph-datasets
One-click install
npx skills add https://github.com/rubensliv/k-dense-ai --skill torch-geometric-rubensliv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torch-geometric
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/torch_geometric
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill torch-geometric-rubensliv

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 provides ready-to-run templates and example code to accelerate building and evaluating graph neural networks using PyTorch Geometric.

Core Features & Use Cases

  • Ready-made model templates (GCN, GAT, GraphSAGE) with training loops and evaluation hooks.
  • Built-in datasets and example workflows for node classification, graph classification, and molecular tasks.
  • Fast prototyping and experimentation for ML researchers and software engineers.

Quick Start

Install PyTorch Geometric and run the provided templates to scaffold a GNN model and run a quick experiment.

Frequently Asked Questions about torch-geometric

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

FAQPage Schema
How do I build and train a Graph Neural Network for node classification?▼

You can build and train a Graph Neural Network for node classification by using provided GCN, GAT, or GraphSAGE templates with built-in datasets and ready-to-run training loops for rapid evaluation.

What is the best way to prototype a GNN for molecular property prediction?▼

Prototyping a GNN for molecular property prediction is best done using PyTorch Geometric templates that provide end-to-end scaffolding, including model definitions and example workflows for molecular tasks.

Can I use GraphSAGE templates for graph classification tasks in PyTorch Geometric?▼

Yes, you can use GraphSAGE templates for graph classification tasks in PyTorch Geometric, as the templates include model definitions and evaluation hooks designed for graph-level predictions.

Do I need to install PyTorch and NetworkX before using these GNN training templates?▼

Yes, you need to install PyTorch and NetworkX before using these GNN training templates, as they are required dependencies alongside PyTorch Geometric, NumPy, and Matplotlib for full functionality.

How does PyTorch Geometric handle graph datasets for rapid model evaluation?▼

PyTorch Geometric handles graph datasets by providing built-in datasets and example workflows that enable fast prototyping and experimentation for machine learning researchers and software engineers.

What GNN architectures are supported by these PyTorch Geometric templates?▼

The PyTorch Geometric templates support GCN, GAT, and GraphSAGE architectures, providing model definitions and evaluation hooks for node classification, graph classification, and molecular tasks.

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