skills.rest
Ritabrata-Chakraborty
Ritabrata-ChakrabortyCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

torch-geometric

Compute and analyze graph neural networks with PyTorch Geometric.

Updated Jun 6, 2026
Search Tags:#PyTorch#GNN#data manipulation#graph neural networks#heterogeneous graphs
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill torch-geometric-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torch-geometric
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/torch-geometric
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill torch-geometric-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torch-geometric, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit addresses the challenge of using PyTorch Geometric effectively for graph neural network (GNN) computations and analysis. It simplifies the process of leveraging the library for graph data manipulation, GNN model construction, and tasks such as node and graph classification.

Core Features & Use Cases

  • Graph Data Manipulation: Handles the creation and manipulation of graph data structures, including homogeneous and heterogeneous graphs.
  • GNN Model Building: Offers step-by-step instructions and best practices for constructing various GNN models.
  • Dataset Usage: Provides examples and instructions for using common graph datasets within PyG.
  • Scaling GNNs: Guides on techniques to scale GNNs to handle large graphs, including neighbor sampling and distributed training.
  • Heterogeneous Graph Learning: Walks through the process of working with heterogeneous graphs, including defining and using HeteroData objects.

Quick Start

To start using PyTorch Geometric for your GNN project, run the following commands to install PyG and set up your environment:

uv pip install torch
uv pip install torch_geometric

Frequently Asked Questions about torch-geometric

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

FAQPage Schema
How do I build graph neural networks in PyTorch Geometric for node classification?▼

PyTorch Geometric streamlines graph neural network computations by offering instructions for GNN model construction and graph data manipulation. It guides you through building models for node and graph classification tasks.

What's the best way to handle heterogeneous graphs in PyTorch Geometric?▼

Handling heterogeneous graphs in PyTorch Geometric involves defining and using HeteroData objects to manage diverse node and edge types. This Skill guides you through constructing and manipulating these structures for heterogeneous graph learning.

How do I scale GNNs to handle large graphs using neighbor sampling?▼

Scaling GNNs to handle large graphs in PyTorch Geometric involves applying neighbor sampling and distributed training techniques. This Skill guides you through these methods to efficiently process extensive graph datasets.

Do I need PyTorch installed to use PyTorch Geometric for graph data manipulation?▼

Yes, you must install PyTorch and PyTorch Geometric to execute graph operations. You can set up your environment and install both dependencies using the uv pip install command before starting your GNN project.

Can PyTorch Geometric be used for social network analysis and bioinformatics?▼

PyTorch Geometric supports social network analysis, bioinformatics, and recommendation systems by enabling efficient graph neural network computations. It empowers researchers to perform node and graph-level learning tasks across these domains.

Related Skills

Explore More Software Engineering Skills→
jackspacejackspace
16

torch-geometric

Develop and train graph neural networks with PyTorch Geometric.

Community
Advanced
K-Dense-AIK-Dense-AI
33.0k

torch-geometric

Build and train graph neural networks with PyTorch Geometric layers.

Official
Advanced
crazymsncrazymsn
22

torch-geometric

Build and evaluate graph neural networks with PyG for node classification, link prediction, and heterogeneous graphs.

Community
Advanced
tassiovaletassiovale
13

torch-geometric

Apply graph neural networks using the PyTorch Geometric library.

Community
Advanced