Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

ArXi:2411.17429v2 Announce Type: replace-cross Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representations indistinguishable. Both phenomena stem from the interaction between message passing and the input topology, ultimately degrading information flow and limiting the performance of GNNs.