  Recent works have investigated the role of graph bottlenecks in preventing
long-range information propagation in message-passing graph neural networks,
causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring
mechanisms have been proposed as preprocessing steps. Graph Echo State Networks
(GESNs) are a reservoir computing model for graphs, where node embeddings are
recursively computed by an untrained message-passing function. In this paper,
we show that GESNs can achieve a significantly better accuracy on six
heterophilic node classification tasks without altering the graph connectivity,
thus suggesting a different route for addressing the over-squashing problem.

Machine Learning