Construct and train sophisticated Graph Neural Network (GNN) models. This course covers advanced GNN architectures, tackles training complexities like scalability and oversmoothing, and provides practical implementation guidance using modern libraries.
Prerequisites GNN Fundamentals, Python, ML
Level:
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).
© 2026 ApX Machine LearningEngineered with