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, while the connections between them are called edges. GNNs leverage the relationships between these nodes to learn patterns and make predictions, making them particularly useful for tasks like social network analysis and recommendation systems. GNNs work by iteratively updating the representation of each node based on its neighbors. This allows the model to capture the local structure of the graph and understand how information flows through it. As a result, GNNs can effectively handle complex data relationships that traditional neural networks may struggle with.