Graph Neural Networks

Graph Neural Networks (GNNs) are a type of artificial intelligence designed to analyze data structured like a network, where items (nodes) are connected by relationships (edges). Unlike traditional neural networks that use fixed structures, GNNs can work with data of various shapes and sizes, making them ideal for tasks like social network analysis, recommendation systems, and molecular chemistry. They learn from the connections and attributes of the data, enabling them to understand complex relationships and make predictions based on the entire graph's structure rather than isolated data points.