Graph neural network

Graph neural networks (GNNs) are a type of neural network designed to perform inference on data structured as graphs, where nodes represent data points and edges represent relationships between them. They utilize a message-passing mechanism to aggregate information from neighboring nodes, allowing for the extraction of complex patterns and relationships within the graph. GNNs are particularly useful for applications such as social network analysis, molecular structure prediction, and recommendation systems.