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graph-neural-networks-basics

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Master GNNs for graph data, unlock hidden insights.

Software Engineering#GNN#graph neural networks#GAT#message passing#GCN#graph data#GraphSAGE
Authortachyon-beep
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill demystifies Graph Neural Networks (GNNs), explaining why traditional CNNs and RNNs fail on irregular graph data. It guides you through the message passing framework and helps you choose the right GNN architecture (GCN, GraphSAGE, GAT) for tasks like molecular property prediction or social network analysis, ensuring you leverage graph structure effectively.

Core Features & Use Cases

  • GNN Architecture Selection: Understand the strengths of GCN (baseline), GraphSAGE (scalability, inductive), and GAT (attention, interpretability).
  • Message Passing Explained: Learn the core mechanism of aggregating neighbor information and updating node representations.
  • Use Case: You're analyzing a social network to predict user interests. This skill helps you decide if GNNs are truly beneficial over simpler models and guides you to GraphSAGE for its inductive capabilities on large, evolving graphs.

Quick Start

I have a large social network graph and need to predict properties of new users. Which GNN architecture should I use?

Dependency Matrix

Required Modules

torchtorch_geometric

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: graph-neural-networks-basics
Download link: https://github.com/tachyon-beep/skillpacks/archive/main.zip#graph-neural-networks-basics

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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