[igorkraw]

GNNs are useful at least in one case, when your data a set of atoms that define your datum through their interactions, specifically a set that is that is high cardinality (so you can't YOLO it with attention) with some notion of neighbourhood (i.e. geometry) within your set (defined by the interactions) which if maintained makes the data permutation equivariant, BUT you can't find a meaningful way to represent that geometry implicitly (for example because it changes between samples) => you YOLO it by just passing the neighourhood/interaction structure in as an input.

In almost every other case, you can exploit additional structure to be more efficient (can you define an order? sequence model. is it euclidean/riemanian? CNN or manifold aware models. no need to have global state? pointcloud networks. you have an explicit hierarchy? Unet version of your underlying modality. etc)

The reason why I find GNNs cool is that 1) they encode the very notion of _relations_ and 2) they have a very nice relationship to completely general discretized differential equations, which as a complex systems/dynamical systems guy is cool (but if you can specialize, there's again easier ways)