That's exactly what I've been working on. Choose it based on the people someone chose to work with and those networks.
It's very much just a single user system that I haven't generalized because that's a way harder problem, the rules aren't generalizable. Classical music for instance, may have 15 names on the credits, a jazz record may have like 4 labels it gets placed on.
Let's take an EP by a popular artist. It may have remixes by popular DJs. Those links are poor quality. Now if it's by an unknown artist, those links usually become high quality.
So do you follow the network of the guy playing the oboe? Maybe? Sometimes weird connections like the album artist is the strong link, sometimes it's compilation albums that one of the songs is placed on. Take say the 1992 release Trancemaster 1: https://www.discogs.com/release/54719-Various-Trancemaster-V... the clustering of those artists is a very strong high quality link. And then there's the "that's what I call music" type compilations where they're worthless.
I can do the music I like because I can narrow the ruleset but a general application is basically a winograd schema challenge because there's a large body of intuition required to weight the network. This task is certainly a nontrivial neural network problem.
Doing it manually with human discretion works. I've got tools for doing that and large labeled data sets I've been working on for 4 years. It just doesn't generalize.
Some day ...