Session 1: GNN Architectures & Graph Attention Networks (3.5 hours)

• Graph Convolutional Networks (GCNs) & Spectral Methods
• Graph Attention Networks (GATs) & Multi-Head Attention
• Neighborhood Sampling Techniques

Hands-on Exercise: Implementing GCNs & GATs

Session 2: Advanced GNN Topics & Deployment (4.5 hours)

• Graph Autoencoders & Variational Graph Autoencoders (VGAE)
• Relational Graph Convolutional Networks (R-GCNs)
• Graph Isomorphism Networks (GINs)
• Scalability Issues & Dynamic Graphs
• Semi-supervised & Self-supervised Learning for GNNs

Hands-on Exercise: Building Explainable GNN Models