graph regression - is that possible?

DATA

• 30 individuals;
• Each individual has an associated graph;
• Individuals graphs represent the connections between brain regions: nodes are brain regions, while edges are the strength of the connection;
• Each individual has an associated scale (accuracy during a task). So they can be ordered from the worst to the best in that scale.

AIM

I want to build a model starting from individual graphs. The model should generate the graph expected, given an accuracy score. So, it sounds like a "graph regression".

I am aware that this is not simple, so not only straight answers but also suggestions on how to approach the problem are appreciated.

[Christine Tobler]

This sounds interesting, but I'm afraid I'm lacking a lot of context. Is the goal to compute a statistically representative individual graph, based on the 30 graphs in your data? Do the 30 graphs all have the same nodes and edges, with different edge weights?