I wrote a little proof of concept in JavaScript showing one way of dealing with the problem [1]. Essentially, as you go through each node in the request, build up a query (say, a SQL query) that represents the fetching of that node by joining it with the query of its parent. Then, stitch the result of running each query back onto the result of running the parent query. Conceptually, this means one resolution function call for each node in the request, rather than one resolution function call for each node node in the response.

[1] https://github.com/mwilliamson/node-graphjoiner

However, it's rather verbose with repeated boilerplate, so to make the approach work I think you'd want to put a higher-level layer on top -- that's what I've done with a Python implementation [2]. I think the general approach is the valuable part, rather than the specific libraries, and seems more in keeping with how I'd normally write queries than the usual DataLoader approach. In particular, I've found it tends to improve performance when fetching large data sets since you don't need to call a resolution function on every single field that gets returned. (DataLoader implementations batch queries together to avoid the n+1 problem, but they mean that you need to implement a batching layer, and probably make your resolution functions asynchronous, which is especially expensive for large data sets).

[2] https://github.com/healx/python-graphjoiner