We identify a smoothness-generalization dilemma in message passing that limits GNN universality across varying homophily and propose the Inceptive Graph Neural Network (IGNN), a universal framework to address the dilemma.

We create a new MPNN with Boundary Conditions of Riemannian Dynamics to combat oversquashing, while allowing for MPNN going deeper.

We understand oversmoothing through the lens of signed graphs and propose a plug-and-play method to address it.