Pretraining a Graph Neural Network (GNN) is a method used to inject domain knowledge into a model prior to fine-tuning it on a specific downstream task. Finding effective pretraining strategies for graphs can be challenging; for instance, using a naive neighborhood reconstruction loss to predict missing edges often fails to improve downstream classification performance. Successful GNN pretraining typically relies on more advanced techniques, such as maximizing mutual information between local and global representations.