The code isn't very performant, but there's lots of room for improvement. It's more a proof of concept, with lots of low-hanging improvements that can be made.
> For context, we have been looking at rgcn and transfer gns for handling identity graphs and event hypergraphs (= big & dynamic heterogeneous property graphs), esp for node classification and edge prediction, and with libs that handle multi-gpu w bigger-than-memory processing. Something like pattern mining / motif mining would also be interesting, which I'm guessing your approach may open up as well :)
There's essentially two sides to the story: the convolutional deep network side and the topological side. The former is is excellently covered by my coauthor in another paper of his (https://arxiv.org/abs/1606.09375), and the latter is covered by many a textbook or intro text (like http://graphics.stanford.edu/courses/cs164-09-spring/Handout...).
> + any libs, esp. scalable?
https://paperswithcode.com/paper/simplicial-neural-networks
The code isn't very performant, but there's lots of room for improvement. It's more a proof of concept, with lots of low-hanging improvements that can be made.
> For context, we have been looking at rgcn and transfer gns for handling identity graphs and event hypergraphs (= big & dynamic heterogeneous property graphs), esp for node classification and edge prediction, and with libs that handle multi-gpu w bigger-than-memory processing. Something like pattern mining / motif mining would also be interesting, which I'm guessing your approach may open up as well :)
Very interesting!