Noise to One, Signal to Another: Under Strong Heterophily, a Separate Self-Path Decides Whether Edges Help a GNN
When edges are removed from a graph, do graph neural networks lose performance because they lose information, or because they lose specific structural signals? We study this with a controlled edge-removal probe. We sparsify graphs from 0% to 95% under three strategies: random removal, removal of cross-label (heterophil...