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.

• External: Understanding of basic machine learning and graph theory concepts
• Internal: None

• 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.

Caution: All text is AI generated