Graph Neural Networks

In this chapter, we will look at a relatively new class of neural networks, the Graph Neural Network (GNN), which is ideally suited for processing graph data. Many real-life problems in areas such as social media, biochemistry, academic literature, and many others are inherently “graph-shaped,” meaning that their inputs are composed of data that can best be represented as graphs. We will cover what graphs are from a mathematical point of view, then explain the intuition behind “graph convolutions,” the main idea behind GNNs. We will then describe a few popular GNN layers that are based on variations of the basic graph convolution technique. We will describe three major applications of GNNs, covering node classification, graph classification, and edge prediction, with examples using TensorFlow and the Deep Graph Library (DGL). DGL provides the GNN layers we have just mentioned plus many more. In addition, it also provides some standard...