What are graph neural networks?

Graph Neural Networks (GNNs) are a type of neural network specifically designed to process data structured as graphs. Unlike traditional neural networks that work with fixed-size inputs such as images or sequences, GNNs can learn from complex relationships and connections found in data like social networks, molecular structures, or transportation systems. By leveraging the graph structure, GNNs can capture both the features of individual nodes and the patterns of their connections, making them highly effective for tasks such as node classification, link prediction, and graph classification.