A Graph Neural Network (GNN) is a type of neural network designed to process graph-structured data, where information is represented through interconnected nodes and relationships. Unlike traditional neural networks that mainly process independent data points, GNNs consider both the features of each node and the connections between nodes.
GNNs learn by passing information between neighboring nodes and combining that information to identify meaningful patterns within the graph. They are widely used in fraud detection, recommendation systems, social network analysis, drug discovery, traffic prediction, cybersecurity, and knowledge graphs, particularly where relationships between entities are as important as the entities themselves.
