[nl]

I like the README.md for StarSpace[1] because it has lots of examples which get you thinking.

I used graph embeddings as input to a classifier to classify people when follower/followee information was easy to gather but text wasn't.

Basically anything that can be represented as a graph can be used. There is some interesting work being done using code syntax trees as input which uses a very similar approach. See code2vec[2]

I'm not aware of any way to transfer text embeddings into graph emneddings, but you can could concatenate them and use them together (I've done this before) or maybe do some dimension reduction or do a multi-task learning thing and try to learn some combined representation.

I'm not ware of the scalability limits for this particular library, but Facebook Research's pytorch-biggraph[3] (released 2 days ago) scales to trillions of edges and billions of nodes.

[1] https://github.com/facebookresearch/StarSpace

[2] https://arxiv.org/abs/1803.09473

[3] https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-...