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Imad-Oute
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torch-geometric

Build graph neural network models with PyTorch Geometric layers and datasets.

Updated May 10, 2026
Search Tags:#library#deep learning#pytorch#dataset#gnn#graph neural network
One-click install
npx skills add https://github.com/Imad-Oute/ResearchForge --skill torch-geometric-imad-oute
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torch-geometric
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/torch_geometric
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill torch-geometric-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive library of graph neural network modules and tools for building, training, and analyzing graph-based machine learning models.

Core Features & Use Cases

  • Implementing GNN architectures: Supports layers like GCN, GAT, GraphSAGE, and more for tasks such as node classification and graph regression.
  • Data handling and dataset loading: Facilitates loading standard datasets like Cora, ENZYMES, QM9, and enables creation of custom datasets.
  • Model training and evaluation: Includes example scripts and boilerplate code for training workflows and performance benchmarking.
  • Use Case: Rapid prototyping of heterogenous and large-scale graph models for research and production deployment.

Quick Start

Import the library modules to construct a graph neural network, load datasets, and execute training scripts to perform classification or regression tasks on graph data.

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 in PyTorch?▼

To build a graph neural network for node classification, import the provided GNN layers like GCN or GAT, load a dataset such as Cora, and execute the training scripts to evaluate model performance on graph-structured data.

Can I use this library to load custom datasets for graph deep learning?▼

Yes, you can load custom datasets for graph deep learning. The library facilitates loading standard datasets like Cora and QM9 while providing data processing tools to create and transform your own graph-structured data.

What graph neural network architectures are available for deep learning workflows?▼

Available graph neural network architectures include GCN, GAT, and GraphSAGE. These layers support deep learning workflows for tasks ranging from node classification to graph regression on heterogeneous and large-scale graphs.

Do I need PyTorch installed to run graph neural network scripts?▼

Yes, you need PyTorch installed to run graph neural network scripts. The library is compatible with PyTorch and requires dependencies including torch-scatter, torch-sparse, torch-cluster, and torch-spline-conv to function.

What is the best way to prototype large-scale graph models for research?▼

The best way to prototype large-scale graph models is using this extensive collection of GNN modules. It streamlines development from data loading to model evaluation, enabling rapid prototyping for research and production deployment.

Does this library support graph regression tasks on molecular datasets?▼

Yes, the library supports graph regression tasks on molecular datasets. It includes data handling tools for standard datasets like QM9 and ENZYMES, along with example scripts for training and benchmarking graph models.

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