Module 23: Graph Neural Networks

Master Graph Neural Networks (GNNs) for learning on graph-structured data.

What You'll Learn

• Graph Fundamentals and Representations
• Message Passing in GNNs
• Graph Convolutional Networks (GCNs)
• Graph Attention Networks (GATs)
• GraphSAGE and Other Architectures
• Applications: Social Networks, Recommendation Systems, Molecular Analysis

Topics Covered

1. Graph Fundamentals

• What are graphs and why they matter
• Graph representations (adjacency matrix, edge list)
• Node features, edge features, graph-level features
• Types of graphs (directed, undirected, weighted)

2. Message Passing

• Core concept of GNNs
• Neighborhood aggregation
• Information propagation
• Multiple layers and depth

3. Graph Convolutional Networks (GCNs)

• Spectral graph convolution
• Spatial graph convolution
• GCN layer implementation
• Training GCNs

4. Graph Attention Networks (GATs)

• Attention mechanism for graphs
• Multi-head attention
• GAT architecture
• Advantages over GCNs

5. Other GNN Architectures

• GraphSAGE: Inductive learning on large graphs
• GIN: Graph Isomorphism Networks
• Graph Transformer: Transformers for graphs
• Gated Graph Neural Networks

6. Applications

• Social Network Analysis
• Recommendation Systems
• Molecular Property Prediction
• Knowledge Graphs
• Traffic Prediction
• Fraud Detection

Learning Objectives

By the end of this module, you should be able to:

• Understand graph data structures and representations
• Implement message passing in GNNs
• Build GCN and GAT models
• Apply GNNs to real-world problems
• Use GNN libraries (PyTorch Geometric, DGL)

Prerequisites

Before starting this module, you should have completed:

• Module 09: Neural Networks Basics
• Module 10: Deep Learning Frameworks (PyTorch recommended)
• Module 12: Natural Language Processing (helpful for understanding attention)

Projects

1. Node Classification: Classify nodes in citation networks
2. Link Prediction: Predict missing edges in social networks
3. Graph Classification: Classify molecular graphs
4. Recommendation System: Build GNN-based recommender
5. Knowledge Graph Embedding: Learn entity and relation embeddings

Key Concepts

• Message Passing: Core mechanism of GNNs
• Neighborhood Aggregation: Combine information from neighbors
• Graph Convolution: Convolution operation on graphs
• Attention: Learn importance of neighbors
• Inductive vs Transductive: Generalize to new graphs vs fixed graph

Documentation & Learning Resources

Official Documentation:

• PyTorch Geometric
• Deep Graph Library (DGL)
• Spektral: Keras/TensorFlow

Free Courses:

• CS224W: Machine Learning with Graphs: Stanford

Complete Detailed Guide →

Additional Resources:

• Advanced Topics →: Graph Transformers, Dynamic Graphs, Heterogeneous Graphs
• Project Tutorial →: Step-by-step node classification
• Quick Reference →: Formulas, architectures, code snippets