The project homepage says "Data scientists and developers can speak the same language now!". So it is surely easier to producitionize a ML project without rewriting the algorithms after the data scientists work out the model with R or Matlab.
It is true. Unfortunately, the project was started several years ago and had nothing to do with the startup world. I would like to complain that VCs destroy another nice name with their hypes :(
In most graph database, you find a vertex by filtering its properties, e.g. Gremlin graph query language. In Unicorn, you can do the similar with document vertices (it is, a vertex corresponding to a document in another table/collection). This is probably very nature in a business application. However, it is not very useful in your case as your vertices are abstract without any properties.
I guess what you want is some large scale graph analytics, which I suggest Spark GrpahX or other distributed graph computing engine.
Unicorn is designed for property directed multi-graphs.
When adding an edge, the end vertices are assumed existed. In your case, we could add a helper function to import a list of edges, similar to Spark GraphX.
http://haifengl.github.io/smile/linear-algebra.html