Graph convolutions – the intuition behind GNNs
The convolution operator, which effectively allows values of neighboring pixels on a 2D plane to be aggregated in a specific way, has been successful in deep neural networks for computer vision. The 1-dimensional variant has seen similar success in natural language processing and audio processing as well. As you will recall from Chapter 3, Convolutional Neural Networks, a network applies convolution and pooling operations across successive layers and manages to learn enough global features across a sufficiently large number of input pixels to succeed at the task it is trained for.
Examining the analogy from the other end, an image (or each channel of an image) can be thought of as a lattice-shaped graph where neighboring pixels link to each other in a specific way. Similarly, a sequence of words or audio signals can be thought of as another linear graph where neighboring tokens are linked to each other. In both cases, the deep...
