Hi @ptrblck,
So I managed to get a balanced accuracy of 80% and F1 score of 79% by adding actual features (biological features) to the network.  I thought that the GNN model could learn the connective properties of the network without node features (i.e identity matrix), but it seems that they are essential in graph representation learning.
Now I encounter another issue with the loss. After training the model for a large number of iterations (400-500), the KLD loss suddenly increases and decreases again. This causes the accuracy to drop from 80% to 71%, before increasing again. I am thinking this could be due to a floating point arithmetic issue.
Here’s an illustration:
Screenshot 2020-06-05 at 23.04.311366×564 53.9 KB
If you have any advice, please let me know.
Thanks again.