Graph neural networks

GNNs are deep learning methods that work on graph-structured data. This family of methods is also known as geometric deep learning and is gaining increasing interest in a variety of applications, including social network analysis and computer graphics.

According to the taxonomy defined in Chapter 2, Graph Machine Learning, the encoder part takes as input both the graph structure and the node features. Those algorithms can be trained either with or without supervision. In this chapter, we will focus on unsupervised training, while the supervised setting will be explored in Chapter 4, Supervised Graph Learning.

If you are familiar with the concept of a Convolutional Neural Network (CNN), you might already know that they are able to achieve impressive results when dealing with regular Euclidean spaces, such as text (one-dimensional), images (two-dimensional), and videos (three-dimensional). A classic CNN consists of a sequence of layers and each layer extracts...