Course info

This course introduces engineers to the fundamentals of graph theory, network science, and advanced topics in graph neural networks (GNNs). The course consists of 16 sessions, each 3 hours long, with additional 2-hour follow-up sessions with teaching assistants.

Course Syllabus

1. Introduction to Graphs and Basic Algorithms
   • Definition and Types of Graphs: Directed, Undirected, Weighted, Unweighted
   • Graph Terminology: Vertices, Edges, Degree, Paths, Cycles
   • Graph Traversal Techniques: BFS, DFS
   • Shortest Path Algorithms: Dijkstra, Bellman-Ford
   • Minimum Spanning Tree: Prim's and Kruskal's Algorithms
2. Network Science and Complex Network Analysis
   • Introduction to Network Science
   • Types of Networks: Social, Biological, Technological Networks
   • Graph Laplacian and Eigen-value Decomposition: Spectral Graph Theory, Eigen-values and Eigen-vectors
3. Measures of Centrality and Community Detection Techniques
   • Measures of Centrality: Degree, Betweenness, Closeness, Eigenvector Centrality
   • Community Detection Techniques: Modularity and Community Structure, Girvan-Newman Algorithm, Louvain Method
4. Introduction to Network Representation Learning
   • Importance of Network Representation
   • Traditional Methods vs. Modern Approaches
   • Node Embeddings: DeepWalk, Node2Vec
   • Edge and Graph Embeddings
5. Matrix Factorization and Deep Learning for Network Representation
   • Matrix Factorization Methods: Adjacency Matrix Factorization, Laplacian Eigenmaps
   • Deep Learning Approaches: Autoencoders for Graphs
6. Advanced Embedding Techniques
   • Variational Graph Autoencoders
   • GraphSAGE
7. Introduction to Graph Neural Networks (GNN)
   • Basic Concepts and History
   • Applications of GNNs
8. Message Passing Neural Networks (MPNN)
   • Message Passing Formulation
   • Aggregation and Update Functions
9. Graph Convolutional Networks (GCN)
   • Convolutional Operations on Graphs
   • Training and Applications of GCNs
10. Graph Attention Networks (GAT)
    • Attention Mechanism in Graphs
    • Implementation and Use Cases
11. Graph Neural Networks with Transformers
    • Incorporating Transformers in GNNs
    • Benefits and Challenges
12. Advanced GNN Architectures
    • Graph Recurrent Networks (GRN)
    • Graph Autoencoders (GAE)
13. Heterogeneous Graph Neural Networks
    • Dealing with Different Types of Nodes and Edges
    • Applications in Multi-Relational Data
14. Applications in Drug Discovery and Healthcare
    • GNNs in Drug-Target Interaction
    • Predictive Models in Healthcare
15. Applications in Social Network Analysis and Recommendation Systems
    • Community Detection in Social Networks
    • Influence and Information Spread
    • GNNs in Recommendation Engines
    • Enhancing Recommendations with Graph Data
16. Case Studies and Future Directions
    • AlphaFold and Protein Structure Prediction
    • Understanding AlphaFold
    • GNNs in Predicting Protein Structures
    • Emerging Trends and Research Areas
    • Ethical Considerations and Challenges

Course Components

• Lectures: Cover theoretical concepts and practical applications.
• Labs: Hands-on exercises and projects to apply learned concepts.
• Quizzes: Periodic assessments to gauge understanding and retention.
• Projects: Real-world projects to demonstrate mastery of topics.
• Readings: Research papers and articles for in-depth knowledge.

Outcome

By the end of this course, participants will have a comprehensive understanding of graph neural networks, from fundamental concepts to advanced applications. They will be equipped with the skills to implement GNNs in various domains and contribute to cutting-edge research and development in the field.