This skill introduces graph neural networks (GNNs), which operate on structured data represented as graphs. It focuses on graph-based learning, message passing, and scaling GNNs on HPC platforms.
Requirements
External: Understanding of basic machine learning and graph theory concepts
Internal: None
Learning Outcomes
Explain how graph neural networks represent and process relational data.
Describe core GNN operations such as message passing and aggregation.
Identify use cases for GNNs in scientific computing, recommendation systems, and bioinformatics.
Apply techniques for batching and sampling large graphs in distributed training.
Evaluate performance and scalability of GNNs in multi-node HPC environments.