[quibono]

I remember reading a bit about GNNs circa 2019. At that time it seemed to have mostly to do with point clouds (for LIDAR data and for 3-D modelling mostly) but I imagine things have changed lots on this front. Are there any interesting papers/resources you could recommend for one to get back up to speed?

[atomflunder]

From what I can tell, the field is indeed evolving very rapidly, but I have only worked on a specific application (knowledge graph completion), so I can't give an overview over all the current day applications. I can, however, recommend William Hamilton's excellent text book, which is available online [1].

[1] https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf

[lmeyerov]

For enterprise relevance in our world, the exciting things have been handling heterogeneity via things like RGCNs, and handling bigger scales via DGL (GPU tricks, sampling tricks, ...). Imagine fraud, hacks, and entity resolution from everything you've recorded on a user interacting with a system.

There are important cases like maps and chemistry that take more specialized techniques, but we focus on events/logs/etc. So less to say on the niche stuff, even if those niches cover big use cases like "how google maps works" or "how google auto-designs their TPUs"

For the logs/events/transactions/clicks/devices/users/accounts cases, happy to chat, but maybe not as useful elsewhere :)