Graph Neural Networks - Learning from Graph-Structured Data

GNNs generalize neural networks to irregular, non-Euclidean data structures by aggregating information from node neighborhoods. From molecules to social networks, GNNs learn powerful representations by passing messages along edges and updating node features iteratively.

• Key point 1 - Message passing framework unifies GCN, GraphSAGE, and GAT architectures
• Key point 2 - Over-smoothing limits practical depth to 2-3 layers in standard GNNs
• Key point 3 - GNNs excel at molecular property prediction, link prediction, and social analysis

"In graphs, structure is the signal - and GNNs learn to read it."