Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain

ArXi:2605.07577v1 Announce Type: new Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph structure, with the resulting performance gain attributed to the rewired adjacency. We find that this attribution may be overstated