  Despite quick progress in the last few years, recent studies have shown that
modern graph neural networks can still fail at very simple tasks, like
detecting small cycles. This hints at the fact that current networks fail to
catch information about the local structure, which is problematic if the
downstream task heavily relies on graph substructure analysis, as in the
context of chemistry. We propose a very simple correction to the now standard
GIN convolution that enables the network to detect small cycles with nearly no
cost in terms of computation time and number of parameters. Tested on real life
molecule property datasets, our model consistently improves performance on
large multi-tasked datasets over all baselines, both globally and on a per-task
setting.
