  Spectral graph convolutional networks are generalizations of standard
convolutional networks for graph-structured data using the Laplacian operator.
A common misconception is the instability of spectral filters, i.e. the
impossibility to transfer spectral filters between graphs of variable size and
topology. This misbelief has limited the development of spectral networks for
multi-graph tasks in favor of spatial graph networks. However, recent works
have proved the stability of spectral filters under graph perturbation. Our
work complements and emphasizes further the high quality of spectral
transferability by benchmarking spectral graph networks on tasks involving
graphs of different size and connectivity. Numerical experiments exhibit
favorable performance on graph regression, graph classification, and node
classification problems on two graph benchmarks. The implementation of our
experiments is available on GitHub for reproducibility.
