Graph Neural Networks

Graph Neural Networks (GNNs) are a class of neural networks designed specifically to work with graph-structured data. They leverage the relationships and connections between nodes in a graph to learn representations that capture the underlying structure and properties of the data. GNNs are characterized by their ability to aggregate information from neighboring nodes, allowing them to effectively model complex interactions and dependencies. Common use cases for GNNs include social network analysis, recommendation systems, and molecular chemistry, where the data can naturally be represented as graphs. With their unique capabilities, GNNs are becoming increasingly popular in various fields, enabling advancements in tasks that involve relational data.