  Information over-squashing is a phenomenon of inefficient information
propagation between distant nodes on networks. It is an important problem that
is known to significantly impact the training of graph neural networks (GNNs),
as the receptive field of a node grows exponentially. To mitigate this problem,
a preprocessing procedure known as rewiring is often applied to the input
network. In this paper, we investigate the use of discrete analogues of
classical geometric notions of curvature to model information flow on networks
and rewire them. We show that these classical notions achieve state-of-the-art
performance in GNN training accuracy on a variety of real-world network
datasets. Moreover, compared to the current state-of-the-art, these classical
notions exhibit a clear advantage in computational runtime by several orders of
magnitude.
