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[D] Generating small graphs using Graph Neural Networks

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

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.