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

1 code implementation • ICLR 2021 • Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, Paul Honeine

Since the graph isomorphism problem is NP-intermediate, and Weisfeiler-Lehman (WL) test can give sufficient but not enough evidence in polynomial time, the theoretical power of GNNs is usually evaluated by the equivalence of WL-test order, followed by an empirical analysis of the models on some reference inductive and transductive datasets.

Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks

2 code implementations • 26 Mar 2020 • Muhammet Balcilar, Guillaume Renton, Pierre Heroux, Benoit Gauzere, Sebastien Adam, Paul Honeine

Moreover, the proposed framework is used to design new convolutions in spectral domain with a custom frequency profile while applying them in the spatial domain.

Graph Classification Graph Learning +1