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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.

✅ 100% Free & Lifetime Access
⏱️ 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.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

PyTorch Geometric
NetworkX
Python
Google Colab
Gephi

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