This has nothing in common with Erlang Programming Language, it's about "method of characterizing geometries based on group theory and projective geometry." https://en.wikipedia.org/wiki/Erlangen_program

(The method is named after Erlangen, the city in Germany.)

Direct link to 156pp paper (an absolutely elegant read):

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

https://arxiv.org/abs/2104.13478

Stanford CS224W: Machine Learning with Graphs

https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjh...

Wow, that paper is 45 MB. I read the article and I now looking at the paper, trying to decide whether to spend a day really absorbing it. I assume that you are recommending it. I really like the typography, use of margin text and math notation, etc.

Here is also a talk about this topic, that Petar Veličković gave virtually at the University Erlangen a few weeks ago (and why it is called Erlangen Programme)

https://www.youtube.com/watch?v=9cxhvQK9ALQ