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