Feedback

Thanks Maulik! The input into the MLP are the neighbouring node features(similar to RGCN). The node feature in this case is not an embedding but multiple samples from the dirichlet distribution.

For instance, sampling from the brazil dirichlet distribution once will yield (P(class=0), p(class=1), ..., p(class=k). We can sample multiple times and concatenate these probabilities to make a "node feature" that will be used in the MLP