Recent analyses of self-supervised representation learning (SSL) find the following data-centric properties to be critical for learning high-quality representations: invariance to task-irrelevant semantics, separability of classes in some latent space, and recoverability of labels from augmented samples. However, given their discrete, non-Euclidean nature, graph datasets and graph SSL methods are unlikely to satisfy these properties. This raises the question: how do graph SSL methods, and in par…