How do graph neural networks work?

Can you explain how graph neural networks work?

Sure! Graph neural networks (GNNs) are designed to process data structured as graphs. They learn to represent nodes and edges in a way that captures the relationships and features of the graph.

What are some applications of graph neural networks?

GNNs are used in various applications, including social network analysis, recommendation systems, and molecular chemistry for drug discovery.

How do GNNs differ from traditional neural networks?

Unlike traditional neural networks that operate on fixed-size inputs, GNNs can handle variable-sized graphs and leverage the connectivity information between nodes.

Can you give an example of a GNN architecture?

One popular architecture is the Graph Convolutional Network (GCN), which applies convolutional operations on graph data to learn node representations.