We focus on the active symmetries of GNNs, and show a bias-variance tradeoff controlled by the choice of symmetry.
We quantify which distances MPNNs induce, leading to a fine-grained understanding of their expressivity.
We propose spectral-inspired GNNs that exploit the advantages from both global and local methods.