In computer science, what are graph neural networks?

In computer science, a graph is a data structure consisting of two components: nodes (vertices) and edges. Normally, a graph can represent things like social media networks, molecules, a sequence of interrelated actions, a circuit, etc. Graph data is so complex that it’s created a lot of challenges for existing machine learning algorithms.

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks. GNNs can do what Convolutional Neural Networks (CNNs) failed to do.