  Self-supervised learning (SSL) of graph neural networks is emerging as a
promising way of leveraging unlabeled data. Currently, most methods are based
on contrastive learning adapted from the image domain, which requires view
generation and a sufficient number of negative samples. In contrast, existing
predictive models do not require negative sampling, but lack theoretical
guidance on the design of pretext training tasks. In this work, we propose the
LaGraph, a theoretically grounded predictive SSL framework based on latent
graph prediction. Learning objectives of LaGraph are derived as self-supervised
upper bounds to objectives for predicting unobserved latent graphs. In addition
to its improved performance, LaGraph provides explanations for recent successes
of predictive models that include invariance-based objectives. We provide
theoretical analysis comparing LaGraph to related methods in different domains.
Our experimental results demonstrate the superiority of LaGraph in performance
and the robustness to decreasing of training sample size on both graph-level
and node-level tasks.
