#3
in
MongoDB
This approach reminds me of RedisGraph[1] (which is now unfortunately EoL).
"RedisGraph is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph."
RedisGraph is a graph database that's built on top of GraphBLAS: https://github.com/RedisGraph/RedisGraph
RedisGraph uses GraphBLAS:
https://github.com/RedisGraph/RedisGraph/
Their paper claims that computing with adjacency (sparse) matrices is more performant than alternatives:
https://arxiv.org/pdf/1905.01294.pdf
They compile queries written in Cypher (Neo4j's language) to operations in linear algebra. An example query is:
MATCH (n:actor{name:"Nicolas Cage"})-[:act]->(m:movie)<-[:act]-(a:actor) RETURN a.name, m.title
dgraph.io uses posting lists.
I think the state of the art is https://github.com/GraphBLAS.
Examples: https://github.com/michelp/pggraphblas & https://github.com/RedisGraph/RedisGraph/