Graph Convolutional Networks (GCNs) are a type of neural network designed to work with data structured as graphs. Unlike traditional neural networks that operate on grid-like data (like images), GCNs can process data where relationships between entities are represented as nodes and edges. This makes them particularly useful for tasks involving social networks, molecular structures, and recommendation systems. GCNs apply convolutional operations to the graph's structure, allowing them to learn features from both the nodes and their connections. By aggregating information from neighboring nodes, GCNs can capture the local structure of the graph, enabling better predictions and classifications based on the relationships within the data.