Graph neural networks (GNNs) are a type of artificial intelligence designed to process data structured as graphs. In a graph, data points are represented as nodes, and the relationships between them are represented as edges. GNNs learn to capture the patterns and features of these connections, making them useful for tasks like social network analysis, recommendation systems, and molecular chemistry. GNNs operate by passing information between connected nodes, allowing them to aggregate and update their representations based on their neighbors. This enables GNNs to effectively model complex relationships and dependencies in data, leading to improved performance in various applications compared to traditional neural networks.