Exercises: Graph Neural Networks

1 Theoretical Exercises

Pen and Paper Exercises

• ✍️ Pen and paper exercises

The pen and paper exercises cover fundamental concepts including:

1. Spectral Graph Theory: Understanding eigenvalues and eigenvectors of graph matrices
2. Fourier Analysis on Graphs: Extending classical signal processing to graph domains
3. Convolution Operations: Defining convolution for irregular graph structures
4. Message Passing: Mathematical formulation of information aggregation in graphs
5. Network Architecture Design: Principles for designing effective GNN architectures

2 Programming Exercises

Coding Exercise: GCN Implementation

Exercise

:class: note

Let’s implement a simple GCN model for node classification. Coding Exercise

This coding exercise will guide you through:

1. Building a GCN from Scratch: Implementing the basic GCN layer
2. Node Classification: Training GCN for semi-supervised node classification
3. Spectral Filtering: Understanding how GCNs relate to spectral graph theory
4. Comparison with Other Methods: Benchmarking against traditional approaches