Deep Graph Infomax (DGI) is a pretraining method for Graph Neural Networks (GNNs) that learns unsupervised node representations. It operates by maximizing the mutual information between local node embeddings $z_u$ and global graph embeddings $z_{\mathcal G}$. This allows the model to capture high-level structural information across the graph, offering an effective alternative to simple neighborhood reconstruction losses.