Let me (try to) be your hero, Marzi. (Insert favorite reference to famous cheezy pop music song, if you like.)
Couldn't you use GraphBLAS algorithms, like they do in RedisGraph (which supports Cypher, btw) to fix that problem with "death star" queries?
Those algorithms are based on linear algebra and matrix operations on sparse matrices (which are like compressed bitmaps on speed, re: https://github.com/RoaringBitmap/RoaringBitmap ). The insight is that the adjacency list of a property-graph is actually a matrix, and then you can use linear algebra on it. But it may require the DB is built bottom up with matrices in mind from the start (instead of linked lists like Neo4j does). Maybe your double array approach in RageDB could be made to fit..
I think you'll find this presentation on GraphBLAS positively mind-blowing, especially from this moment: https://youtu.be/xnez6tloNSQ?t=1531
Such math-based algorithms seem perfect to optimally answer unbounded (death) star queries like “How are you connected to your neighbors and what are they?”
That way, for such queries one doesn't have to traverse the graph database as a discovery process through what each node "knows about", but could view and operate on the database from a God-like perspective, similar to table operations in relational databases.
Further reading: https://graphblas.org/
It's a bit too complicated to totally summarize here, but it uses a bit per object in the scene. Then bit-wise operations are used to perform quick set operations on objects.
This data structure got me generally interested in algorithms that use bits for set operations. I found the Roaring Bitmap github page has a lot of useful information and references wrt this topic: https://github.com/RoaringBitmap/RoaringBitmap
Lamire (the author of the blog post) is well know as the author of https://github.com/RoaringBitmap/RoaringBitmap which is in widespread use.