I have been looking at using Graph Neural Networks as a classifier. The example here: https://towardsdatascience.com/hands-on-graph-neural-networks-with-pytorch-pytorch-geometric-359487e221a8 was a good intro for me – provided with lots of small graphs (Recsys 2015 yoochoose challenge data) can you make a prediction on what they will buy. This seems to get good results (I am unsure how it was appropriate to use a variant of GraphSage though, the documentation recommends it to be used on very large graphs – is there any suggestions as to why this was ok here?).

However, what if I want to go a step further and generate new graphs? How could this be accomplished? One generative graph approach, GraphGAN, is designed to be trained on 1 very large graph, as opposed to lots of smaller ones. Is there work that looks at doing what I am hoping to accomplish?

Thanks