What are graph neural networks (GNNs)?

Graph Neural Networks generalize neural networks to graph-structured data where nodes have features and edges represent relationships. They work via message passing: each node aggregates (e.g., sums or averages) feature vectors from its neighbors and updates its own representation. Multiple rounds of message passing allow nodes to incorporate information from multi-hop neighborhoods. Types: GCN (Graph Convolutional Networks), GraphSAGE, GAT (Graph Attention Networks). Applications: molecular property prediction (drug discovery), social network analysis, recommendation systems, and knowledge graph completion.