There's some really interesting research going on in the deep learning world where they are trying to find the best way to represent graphs in vector space.

For example hyperbolic embeddings for trees: https://dawn.cs.stanford.edu/2018/03/19/hyperbolics/

I wonder if this book will be useful for that purpose? (the term "vector labeling" came up a few hundred times but I am not sure if that's the same thing)

Once I saw the author's name I saved, thanks!

The problem i have with graph databases in geometry is that you have to keep track of all of the angles. Especially for stuff like chemistry where part of the shape can rotate independently from the rest of the shape.

The pdf is about representing graphs with geometry, not representing geometry with graphs.

RocketSyntax is talking about representing graphs with geometry too, right?