Graph Neural Networks & GNNs with PyTorch Geometric

Master graph neural networks for structured data. Build systems that reason over graphs: knowledge graphs, social networks, molecular structures.

InstructorMaxime Labonne

Duration14+ hours

CostFree

About the Instructor

Maxime Labonne & PyG Community

Open-source GNN course with 5K+ GitHub stars. PyTorch Geometric library used by DeepMind, Meta, and 1000+ AI teams.

First Principles

Graphs capture relationships: social connections, molecular bonds, transaction networks. GNNs learn embeddings by aggregating information from neighboring nodes. This is how Google's Knowledge Graph and Meta's recommendation system work.

AI Ecosystem Fit

Layer 4 (Models) — Specialized for structured/relational data. 30% of L4/L5 jobs involve graphs (recommendations, fraud, molecules).

Prerequisites

PyTorch fundamentals (FastAI or Stanford) Understanding of convolutions and neural networks

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What Paid Projects This Unlocks

Build graph AI: recommendation systems, molecular modeling, knowledge graph reasoning, anomaly detection in networks.

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3 Client Projects You Can Get Paid For

Real businesses have this problem right now. Build it, pitch it, charge for it.

Stack This With → (Unlocks Higher-Paying Projects)

Apply to real-world graphs (social, knowledge, molecular)

Combine with transformer attention mechanisms

Deploy GNN models at scale

Why This Matters

Graph AI is the fastest-growing specialization. Every recommendation system, fraud detector, and molecular simulator uses GNNs. This skill is worth 10%+ premium.