What are graph neural networks (GNNs) and why are they significant?

Graph neural networks (GNNs) are a class of neural networks designed to process data represented as graphs, where nodes represent entities and edges represent relationships. GNNs are significant because they can capture complex relationships and dependencies inherent in graph-structured data, making them suitable for applications like social network analysis, molecular chemistry, and recommendation systems. They leverage message passing mechanisms to learn node representations by aggregating information from neighboring nodes, allowing the model to learn global context while maintaining local structure.