Understanding graph convolutions
The previous section showed how graph data can be represented. The next logical step is to discuss what tools we have that can effectively utilize those representations.
In the following subsections, we will introduce graph convolutions, which are the key component for building GNNs. In this section, we’ll see why we want to use convolutions on graphs and discuss what attributes we want those convolutions to have. We’ll then introduce graph convolutions through an implementation example.
The motivation behind using graph convolutions
To help explain graph convolutions, let’s briefly recap how convolutions are utilized in convolutional neural networks (CNNs), which we discussed in Chapter 14, Classifying Images with Deep Convolutional Neural Networks. In the context of images, we can think of a convolution as the process of sliding a convolutional filter over an image, where, at each step, a weighted sum is computed...
