Using a graph database to store and retrieve sorted users by personality scores

Question

I am interested in storing a set of users that have personality scores. I would like to get them to be more connected (closer?) to each other based on formulas that are applied to their scores. The more similar the users are, the more connected or closer to each other they are (like in a cluster). The closest nodes are to one-another, the more similar they are.

I currently do this over multiple steps (some in SQL and other in code) from a relational database.

Most posts out there and documentation seems to focus on how to get started and what the advantages are at a high level compared to relational databases.

I am wondering if Graph databases are better suited for this and would do most of the heavy lifting out of the box or more natively. Any details are greatly appreciated.

Answer 1

You could consider modeling it like this:

Where a vertex type/label named Score_range was introduced, together with the label User(with property score).

User vertices are connected to Score_range vertex like User with score: 101 is connected to Score_range(vertexID=100) which stands for [100, 110).

Thus, those vertices with closer score are more connected/clusterred in this graph, and in your applicaiton, you need to make connection changes when the score are recaculated/changed to the graph database.

Then, either to run cluster algorithm(i.e. Louvain) on the whole graph or graph query to find path between any two user nodes(i.e. FIND PATH in Nebula Graph, an opensource distributed graph database speaks opencypher), the closeness will be reflected.

But, I think due to this connection/closness is actually numerical/sortable, simply handling this closeness relationship may not need a graph database from the context you already provided.

PS. I drew a picture of a graph in the above schema: