Are there sets of best practices to approach how to model data in a graph database (I am considering arangodb right now but the question would apply to other platforms)? Here is a practical case to illustrate my question:
Assuming we are creating a centralised contact list for users. Each user has contacts but some contacts could be common to users e.g. John knows Mary, and Marc knows Mary. I would thus have 3 nodes (John, Mary and Marc) but John should only see his relationship to Mary, not Marc's relationship to Mary
So how should a full graph be designed in order to support user access to their information?
Option 1: Create 1 graph per user. That way, I know exactly who can see what (I could for example prefix all my collections with the user id). That would be simple but would duplicate a lot of data (e.g. if I put all my family in the db, my brother will do too, creating twice the same data, in different graphs)
Option 2: Create 1 general graph with Contact nodes, plus User nodes. I would have the contact John, Mary and Marc connected, but the User node representing John, would be linked to the Contact nodes John and Mary only. That way I would know to get only the contact nodes that are connected to the User node I am focusing on. The problem is that edges cannot be linked to the User node (I cannot have an edge going from a node to an edge...can I?). So I would have to add an attribute of user_id to all the edges in order to only fetch the ones relevant to the current user. This is slightly nicer as I do not have to duplicate nodes, but I would still have to duplicate edges as they would be user specific
Option 3: Do it SQL like with a Rights table, maintaining a list of Contact ids along with what user can see what Node and what Edge (heavy on joins)
Options 4: ???
As in everything, there are many ways to reach a solution but I was wondering what was considered best practice to balance cleanliness of approach and performance for insertion/deletion...knowing that performance might be platform dependent