Graph Neural Networks

Graph Neural Networks are a class of deep learning models designed to process graph-structured data. A graph consists of nodes (entities) and edges (relationships between entities). Unlike images (grid structure) or sequences (linear), graphs have arbitrary connectivity. GNNs learn representations by iteratively aggregating information from neighboring nodes. After K layers, each node has learned embeddings that encode its own features and the structure of its K-hop neighborhood. These embeddings power downstream tasks: node classification (predict node type), link prediction (will two nodes connect?), and graph classification (is this molecule stable?).