In my use case I care not one bit about the overall graph structure, but only about the inter-node interactions/relationships. But often times you will get graphs where you have 1% type A interactions, 12% type B interactions and 87% type C interactions and that makes training quite challenging. But I am not sure if you can stay that that is over-fitting the graph structure rather than simply learning the most common interactions in the dataset.