  Random data augmentations (RDAs) are state of the art regarding practical
graph neural networks that are provably universal. There is great diversity
regarding terminology, methodology, benchmarks, and evaluation metrics used
among existing RDAs. Not only does this make it increasingly difficult for
practitioners to decide which technique to apply to a given problem, but it
also stands in the way of systematic improvements. We propose a new
comprehensive framework that captures all previous RDA techniques. On the
theoretical side, among other results, we formally prove that under natural
conditions all instantiations of our framework are universal. On the practical
side, we develop a method to systematically and automatically train RDAs. This
in turn enables us to impartially and objectively compare all existing RDAs.
New RDAs naturally emerge from our approach, and our experiments demonstrate
that they improve the state of the art.
