oversmoothing

This paper analyzes oversmoothing in Neural Sheaf Diffusion (NSD) as a representation degeneracy phenomenon using quiver theory and Geometric Invariant Theory. It proposes moment-map-inspired regularizers and explores non-uniform stalk dimensions to mitigate this issue in heterophilic graph benchmarks.

This paper introduces HMH, a hierarchical multi-scale Graph Neural Network framework designed to address oversmoothing and oversquashing in heterophilous graphs. It utilizes spectral filters with Haar bases to achieve scalable learning and improved performance on node and graph classification tasks.