Prerequisites: Proficiency in Python and deep learning frameworks (PyTorch/TensorFlow). Strong understanding of machine learning concepts, deep learning fundamentals (CNNs, RNNs), and linear algebra. Familiarity with basic graph theory is beneficial.

Level: Advanced

What You'll Learn

• Advanced GNN Architectures

  Understand and implement complex GNN models including Graph Attention Networks (GAT) and Graph Transformers.

• Spectral and Spatial GNNs

  Analyze the theoretical underpinnings and practical differences between spectral and spatial GNN approaches.

• GNN Training Challenges

  Address common GNN training problems such as oversmoothing, oversquashing, and scalability for large graphs.

• Scalability Techniques

  Implement graph sampling and clustering techniques (e.g., GraphSAINT, Cluster-GCN) for training GNNs on massive datasets.

• Advanced Graph Applications

  Apply GNNs to complex tasks like heterogeneous graphs, dynamic graphs, and graph generation.

• Implementation Best Practices

  Optimize GNN implementations using libraries like PyTorch Geometric (PyG) and Deep Graph Library (DGL).