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Graph Neural Networks

Unlock the power of relational data. Learn to build Graph Neural Networks for social networks, molecule discovery, and recommendation systems.

Standard neural networks assume independent data, but the world is connected. Graph Neural Networks (GNNs) learn from the relationships (edges) between data points (nodes). This cutting-edge course teaches you to process graph data using PyTorch Geometric. You will build models for node classification (e.g., detecting fraud users), link prediction (e.g., recommending friends), and graph classification (e.g., predicting molecule toxicity). This is a rapidly growing field in drug discovery and social network analysis.

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⏱️ 5-Minute Lessons (Bite-sized learning)

🚀 21-Lesson Path (Independent modules)

📱 Mobile Friendly (Learn anywhere)

Graph AI

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Complete Course Syllabus

1

Graph Data Basics

Nodes, edges, adjacency matrices, and graph properties.
2

GNN Architecture

Message passing and aggregation strategies in graphs.
3

Node Classification

Predicting properties of individual nodes in a network.
4

Link Prediction

Predicting future or missing connections between nodes.
5

PyTorch Geometric

Implementing scalable GNNs with the standard library.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary

$140k - $190k

Career Paths

Graph AI Researcher $150k-$200k

Drug Discovery Scientist $140k-$190k

Recommendation Systems Eng $135k-$180k

What You Will Learn

Implement Graph Convolutional Networks (GCNs) for relational data

Perform node classification to label entities in a network

Predict missing links in social or knowledge graphs

Classify entire graphs for applications like chemistry

Utilize PyTorch Geometric for efficient graph deep learning

Skills You Will Gain

Graph Neural Networks PyTorch Geometric Graph Theory Link Prediction Node Embeddings

Who Is This For

AI Researchers

Bioinformatics Devs

Recommendation Engs

Prerequisites

Deep Learning

Graph Theory Basics

Graph Neural Networks FAQs

Niche topic?

Growing fast in pharma, social media, and fraud.

Hard math?

Requires understanding matrix multiplication well.

Applications?

Drug discovery, recommendations, fraud detection.

Prerequisites?

Must know Deep Learning and PyTorch basics.

PyTorch Geometric

NetworkX

Python

Google Colab

Gephi

Your Learning Path

Master This Topic, Step by Step

Go from your first step to master level. Three simple steps, all about Graph Neural Networks.

Currently studying: Graph Neural Networks

Step 1 · First Step

Graph Neural Networks Basics

Build a strong base in Graph Neural Networks. No experience needed — just start.

5 min Free to explore

Graph Neural Networks for Beginners

Learn the basics — 5 minutes is all it takes

Step 2 · Keep Going

Advanced Graph Neural Networks

Know the basics? Good. Now learn the parts of Graph Neural Networks most people never reach.

5 min Free to explore

Advanced Graph Neural Networks Course

Ready for more? Take the next step

Step 3 · Master With AI

Master Graph Neural Networks with AI

The final step: let Hugging Face AutoTrain fine-tune models without writing code while you focus on the big picture.

5 min Free to explore

AI Model Training

Take the shortcut — let AI do the heavy lifting

Start with Step 1. Each step builds on the one before it.