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Graph Echo State Networks (GESN) have already demonstrated their efficacy andefficiency in graph classification tasks. However, semi-supervised nodeclassification brought out the problem of over-smoothing in end-to-end traineddeep models, which causes a bias towards high homophily graphs. We evaluate forthe first time GESN on node classification tasks with different degrees ofhomophily, analyzing also the impact of the reservoir radius. Our experimentsshow that reservoir models are able to achieve better or comparable accuracywith respect to fully trained deep models that implement ad hoc variations inthe architectural bias, with a gain in terms of efficiency.

Beyond Homophily with Graph Echo State Networks