Graph Neural Networks (GNNs) are a class of neural networks designed to perform inference on data structured as graphs, leveraging the relationships and interactions between nodes to improve learning tasks. They are particularly effective in domains where data is naturally represented as graphs, such as social networks, molecular chemistry, and recommendation systems, by iteratively updating node representations based on their neighbors.