[lmeyerov]

Agreed, the definitions here of what a GNN is (=> cannot do) seem pedantically narrow & extreme, and then throws the baby out w the bath water

See last ~2 years of work by Michael Bronstein's teams (who moonlights as Twitter's ex? GNN r&d lead) that address 2+ of the points here

It's true they are not the end-all, but our issues in practice aren't the above, but more like how to best combine temporal deep learning techniques w graph ones (it is not just another continuous dimension)

Worth noting: TDA methods & UMAP are largely the same under-the-hood & in practice, and we find UMAP (incl neural) highly effective, and typically reach for it before GNNs. UMAP has been an admirably rare mix of theory & practicality..

[dpflan]

Interesting. Could one graph be the topological representation, let's say like SCC? So there are heterogenous graphs with meta-graphs connected to them...?

[lmeyerov]

There are a lot of cool experiments happening like this, both principled and from intuition

Ex:Label prop following non-local jumps ("metapaths") based on community detection (network-of-network, ...) or other shapes. Same thing for what constitutes a feature.

Michael Bronstein's papers (and talks!) are often in this vein, in the mainstream, leading, and quite accessible, so would recommend starting there..