In my opinion there aren't many deep learning applications that are fundamentally snake-oil, it's more that people who don't quite understand the limitations and advantages of particular methods that end up being (witting or unwitting) snake-oil salespeople.
Deep graph NN's are very useful for lots of data that are inherently graph-structured (another commenter above mentioned chemistry applications, and there are lots of other examples). Whether or not for the time-being they give SOTA results on common datasets, being able to work directly with graph-structured data is quite appealing in many cases.
Deep graph NN's are very useful for lots of data that are inherently graph-structured (another commenter above mentioned chemistry applications, and there are lots of other examples). Whether or not for the time-being they give SOTA results on common datasets, being able to work directly with graph-structured data is quite appealing in many cases.