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torch-geometric

Develop and train Graph Neural Networks using PyTorch Geometric.

2|Updated Jun 4, 2026
Search Tags:#deep learning#pytorch#neural networks#gnn#geometric deep learning#graph learning
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill torch-geometric-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torch-geometric
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/torch-geometric
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill torch-geometric-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torch_geometric, torch-scatter, torch-sparse, torch-cluster, and includes references (resource) components.

What problem does it solve?

This skill simplifies the complex implementation of Graph Neural Networks (GNNs) by providing a standardized framework for handling graph data structures, message passing, and scalable training on large-scale relational datasets.

Core Features & Use Cases

  • Graph Data Handling: Provides specialized data structures for homogeneous and heterogeneous graphs, including support for node, edge, and graph-level tasks.
  • GNN Architectures: Offers a comprehensive library of pre-built layers like GCN, GAT, and SAGE, alongside a flexible API for custom message-passing implementations.
  • Scalability: Includes advanced loaders like NeighborLoader for training on massive graphs that exceed GPU memory limits.

Quick Start

Use the torch-geometric skill to build a GCN model for node classification on my graph dataset.

Frequently Asked Questions about torch-geometric

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

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

To build a Graph Neural Network for node classification, you use a standardized framework providing pre-built layers like GCN and flexible message-passing APIs to process relational data structures efficiently.

Can I train Graph Neural Networks on large graphs that exceed GPU memory?▼

Yes, you can train Graph Neural Networks on massive graphs exceeding GPU memory limits using advanced scalable loaders like NeighborLoader for efficient mini-batch training routines.

Does this framework support heterogeneous graph structures for link prediction?▼

Yes, this framework supports heterogeneous graph structures for link prediction, providing specialized data structures to handle complex relational data analysis across diverse graph types.

What is the message-passing paradigm in geometric deep learning?▼

The message-passing paradigm in geometric deep learning is a mechanism where nodes aggregate features from their neighbors, implemented via a flexible API to update node representations in graph neural networks.

Do I need PyTorch to use pre-built GNN architectures like GAT and SAGE?▼

Yes, you need PyTorch and associated libraries like torch-scatter and torch-sparse to use pre-built GNN architectures like GAT and SAGE for relational data analysis.

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