Author here to clear up a few questions: I did not run any benchmarks for Memgraph, just Neo4j on my machine and compared them to their numbers on their machine. My 8 faster cores to their 12 slower cores, so not apples to apples, but close enough to make the point that Memgraph is not 120x times faster than Neo4j. I used to work at Neo4j, then at AWS for Neptune, I work on my own graph database http://ragedb.com/, and work for another database company https://relational.ai/

If you want to be my hero, find a way to fix this problem: https://maxdemarzi.com/2023/01/09/death-star-queries-in-grap...

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/