Session 1: Graph Theory & Deep Learning (3.5 hours)

• Basic Graph Theory: Nodes, Edges, Directed & Undirected Graphs
• Graph Representation: Adjacency Matrix, Adjacency List, Edge List
• Common Graph Algorithms: DFS, BFS, Dijkstra’s Algorithm
• Neural Network Basics & Why GNNs for Graph Data

Hands-on Exercise: DFS & BFS on Graph Data

Session 2: Foundations of Graph Neural Networks (4.5 hours)

• What is a Graph Neural Network (GNN)?
• Graph Representation Learning & GCNs
• Graph Message Passing & Aggregation Functions
• Node Embedding & Graph Embedding

Hands-on Exercise: Implementing Graph Convolution Networks (GCNs)