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Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs. Current algorithms are based on contrastive learning, which is computationally and memory expensive, and the assumption of invariance under certain graph augmentations. However, graph transformations such as edge sampling may modify the semantics of the data, potentially leading to inaccurate invariance assumptions. To address these limitations, we introduce Regularized Graph Infomax (RGI), a simple yet effective framework for node level self-supervised learning that trains a graph neural network encoder by maximizing the mutual information between the output node embeddings and their propagation through the graph, which encode the nodes' local and global context, respectively. RGI generates self-supervision signals through feature propagation rather than relying on graph data augmentations. Furthermore, the method is non-contrastive and does not depend on a two branch architecture. We run RGI on both transductive and inductive settings with popular graph benchmarks and show that it can achieve state-of-the-art performance regardless of its simplicity. • We present Regularized Graph Infomax (RGI) for efficient graph self-supervised learning. • RGI generates self-supervision signals with feature propagation. • The method does not use graph data augmentations and is non-contrastive. • State of the art performance is achieved in both transductive and inductive tasks. [ABSTRACT FROM AUTHOR]