expanding on that: people used to do stuff like pagerank etc. ahead of time and decorate their tabular data with the results: entities, events, whatever for social, fraud, security, customer journey, whatever. that meant a phase separation between graph analytic enrichments and learning. bringing the non-local graph reasoning to the learning phase in a way that isn't slow enables closing the loop.
The early graphsage stuff was, afaict, proven for generic social recommendors, but most gnn's I see seem pretty custom (e.g., deepmind's protein folding solution), esp. when not prohibitively slow. It sounds like more generic use is becoming practical w/ these libs, and esp. interesting to me, the latest NIPS had graph transformers papers, which brings another level of practically here. Not sure if DGL & friends have those yet..
The early graphsage stuff was, afaict, proven for generic social recommendors, but most gnn's I see seem pretty custom (e.g., deepmind's protein folding solution), esp. when not prohibitively slow. It sounds like more generic use is becoming practical w/ these libs, and esp. interesting to me, the latest NIPS had graph transformers papers, which brings another level of practically here. Not sure if DGL & friends have those yet..