  In this paper we show that simple noise regularisation can be an effective
way to address GNN oversmoothing. First we argue that regularisers addressing
oversmoothing should both penalise node latent similarity and encourage
meaningful node representations. From this observation we derive "Noisy Nodes",
a simple technique in which we corrupt the input graph with noise, and add a
noise correcting node-level loss. The diverse node level loss encourages latent
node diversity, and the denoising objective encourages graph manifold learning.
Our regulariser applies well-studied methods in simple, straightforward ways
which allow even generic architectures to overcome oversmoothing and achieve
state of the art results on quantum chemistry tasks, and improve results
significantly on Open Graph Benchmark (OGB) datasets. Our results suggest Noisy
Nodes can serve as a complementary building block in the GNN toolkit.
