Graph neural networks
Graph neural networks are machine-learning models built for data made of nodes and edges. They learn from both the attributes of each item and the pattern of relationships around it, which makes them useful for recommendations, molecules, knowledge graphs, fraud detection, traffic systems, and other connected data.