Graph neural networks (GNNs) are a type of neural network designed specifically to handle data that can be represented as a graph, which consists of nodes connected by edges. This type of data is common in many real-world applications, such as social networks, recommendation systems, and chemical molecules. GNNs have the ability to learn representations of nodes and relationships between them, allowing them to make predictions or classifications based on the graph structure. They achieve this by aggregating information from neighboring nodes during the learning process, enabling them to capture complex patterns and dependencies in the data. Overall, GNNs have proven to be effective in a wide range of applications, including node classification, link prediction, and graph classification, making them a valuable tool for analyzing and extracting insights from graph-structured data.