Graph customizations
We have seen how to build and train GNNs for common graph ML tasks. However, for convenience, we have chosen to use prebuilt DGL graph convolution layers in our models. While unlikely, it is possible that you might need a layer that is not provided with the DGL package. DGL provides a message passing API to allow you to build custom graph layers easily. In the first part of this section, we will look at an example where we use the message-passing API to build a custom graph convolution layer.
We have also loaded datasets from the DGL data package for our examples. It is far more likely that we will need to use our own data instead. So, in the second part of this section, we will see how to convert our own data into a DGL dataset.
Custom layers and message passing
Although DGL provides many graph layers out of the box, there may be cases where the ones provided don’t meet our needs exactly and we need to build your own.
Fortunately, all...
