A syllabus for learning GNNs

1. Introduction to Graph Theory and Networks

• What are graphs and networks?

• Basic properties and types of graphs

• Graph theory concepts

• Graph representations (adjacency matrix, adjacency list)

• Resource: Introduction to Graph Theory | Brilliant

2. Python Basics and Libraries for Graphs

• Python revision (if required)

• Introduction to NetworkX for creating and manipulating networks

• Visualization of graphs in Python

• Resource: NetworkX Tutorial

3. Introduction to Graph Neural Networks (GNNs)

• Why use GNNs?

• What are GNNs?

• How do GNNs work?

• Resource: Introduction to Graph Neural Networks | Medium

4. Graph Convolutional Networks (GCNs)

• Understanding GCNs

• How do GCNs work?

• Implementing a simple GCN with Python

• Resource: How to do Deep Learning on Graphs with Graph Convolutional Networks | Medium

5. Advanced Topics in GNNs

• Graph Attention Networks (GATs)

• GraphSAGE

• Graph Isomorphism Networks (GINs)

• Heterogeneous Graph Neural Networks

• Scalability and Efficiency in GNNs

• Resource: Graph Neural Networks: A Review of Methods and Applications | Medium

6. Practical Applications and Case Studies

• GNNs for social network analysis

• GNNs for recommendation systems

• GNNs for fraud detection

• GNNs for natural language processing

• Resource: Applications of Graph Neural Networks | Medium

7. Hands-on Projects

• Implementing GNNs for a chosen dataset/problem

• Resource: PyTorch Geometric (PyG) Tutorial

8. Further Readings and Advanced Resources

• Latest research papers and articles on GNNs

• Resource: A Comprehensive Survey on Graph Neural Networks | IEEE