Are you doing some regular like vision? For the reasons you're saying, I don't think it's an accident that GNNs are popular mostly in domains like recommendati

Are you doing some regular like vision? For the reasons you're saying, I don't think it's an accident that GNNs are popular mostly in domains like recommendations that feel graph-y for their domain model so getting to a useful topology isn't as big a leap. A frustration for me has been more that many of these graph-y domains are about behavioral machine/people data like logs that contain a large amount of categorical dimensions. The graph part can help, but it is just as import to key on the categorical dimensions, and doing well there often end up outside of the model - random forest etc. It's easier to start with those, and then is a lot of work for the GNN part (though we & others have been trying to simplify) for "a bit more lift". Of course, if this is your core business and this means many millions of dollars, it can be justified... but still, it's hard for most production teams. In practice, we often just do something with pygraphistry users like xgboost + umap and move on. Even getting an RGCN to perform well takes work..