Summary

In this chapter, we have covered graph neural networks, an exciting set of techniques to learn not only from node features but also from the interaction between nodes. We have covered the intuition behind why graph convolutions work and the parallels between them and convolutions in computer vision. We have described some common graph convolutions, which are provided as layers by DGL. We have demonstrated how to use the DGL for popular graph tasks of node classification, graph classification, and link prediction. In addition, in the unlikely event that our needs are not met by standard DGL graph layers, we have learned how to implement our own graph convolution layer using DGL’s message-passing framework. We have also seen how to build DGL datasets for our own graph data. Finally, we look at some emerging directions of graph neural networks, namely heterogeneous graphs and temporal graphs. This should equip you with skills to use GNNs to solve interesting problems in...