2 papers
cs.LG2026
Generalization Bounds for Spectral GNNs via Fourier Domain Analysis
Vahan A. Martirosyan, Daniele Malitesta, Hugues Talbot +2
Spectral graph neural networks learn graph filters, but their behavior with increasing depth and polynomial order is not well understood. We analyze these models in the graph Fouri…
cs.LG2025
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan +2
Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning. The expressivity of Graph Neural Net…