Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective

Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective, Muhammet Balcilar (LITIS).
Normastic workshop, february 2021.

Abstract: In the recent literature of Graph Neural Networks (GNN), the expressive power of models has been studied through their capability to distinguish if two given graphs are isomorphic or not. However, such analysis does not account the signal processing pipeline, whose capability is generally evaluated in the spectral domain. In this presentation, we argue that a spectral analysis of GNNs behavior can provide a complementary point of view to go one step further in the understanding of GNNs.