Graph Neural Networks

Graph Neural Networks (GNNs) are a class of neural networks designed to process data structured as graphs, where entities are represented as nodes and their relationships as edges. They leverage the connectivity and topology of the graph to learn representations of nodes and entire graphs, enabling the model to capture complex dependencies and interactions within the data. GNNs are particularly effective in tasks that involve relational data, allowing for the propagation of information across the graph structure.