Machine Learning
Advanced
Graph Neural Networks in Practice
Model complex relational graph data using PyTorch Geometric and GNN architectures.
Course Overview
Understand node embeddings, graph convolutions, link prediction, and sub-graph classification for fraud detection, recommendation engines, and biological networks.
What You Will Learn
Represent complex relational domain data as nodes, edges, and graph matrices
Implement Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT)
Execute node classification, link prediction, and graph embedding tasks in PyTorch Geometric
Scale GNN training to large graphs using neighbor sampling techniques
Deploy GNN model endpoints for real-time recommendation and fraud detection
Tools & Technologies Used
PyTorch
PyTorch Geometric (PyG)
NetworkX
Python 3.11
Scikit-Learn
Structured Curriculum
2 Modules · 4 Lessons · 250 Minutes Total
Module 1
Module 1: Graph Representation & Message Passing
2 lessons
Module 1: Graph Representation & Message Passing
Formulate graph objects and master node message-passing mechanics.
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Graph Topology & Adjacency Matrices
Map graphs into PyTorch Geometric Data structures with edge indices and feature matrices.
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Message Passing & Graph Convolutions (GCN)
Implement custom message-passing layers to aggregate node neighbor features.
Module 2
Module 2: Graph Attention & Production Scale
2 lessons
Module 2: Graph Attention & Production Scale
Use attention mechanisms and scale GNNs to multi-million edge networks.
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Graph Attention Networks (GAT)
Apply self-attention over node neighborhoods to weigh relational importance.
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Neighbor Sampling & Large-Graph Training
Train GNNs on graphs that exceed GPU memory using PyG NeighborLoader.
Practical Project & Capstone Outcome
🚀 Capstone Project
E-Commerce Fraud Detection Graph Engine
Construct a Graph Neural Network on a heterogeneous user-transaction graph to predict fraudulent accounts with high precision.
Prerequisites
- Python proficiency
- Solid understanding of PyTorch and linear algebra
Intended Audience
- Data Scientists working with network, social, or molecular graph datasets
- ML Engineers building graph-based recommendation systems
Instructor Information
P
Prof. Julian Kross
Course Author & Industry Expert
Prof. Julian Kross is a Graph AI Researcher who has published extensive work on scalable graph neural network architectures.
Frequently Asked Questions
Do I need prior experience with PyTorch Geometric?
No. Standard PyTorch knowledge is required, but PyTorch Geometric is introduced from the ground up.