Feedback

Hi Obada, Great article. I just wanted to understand what did you mean by "Using multiple samples of probabilities 1,2, and 3, we can form a node feature for the source node."?

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