Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs. We introduce \textit{Regularized Graph Infomax (RGI)}, a simple yet effective framework for node level self-supervised learning on graphs that trains a graph neural network encoder by maximizing the mutual information between node level local and global views, in contrast to previous works that employ graph level global views. The method promotes the predictability between views while regularizing the covariance matrices of the representations. Therefore, RGI is non-contrastive, does not depend on complex asymmetric architectures nor training tricks, is augmentation-free and does not rely 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.

翻译：自监督学习作为避免在图表示学习中依赖大量人工标注的解决方案正受到广泛关注。我们提出**正则化图信息最大化（RGI）**，一种简单而有效的节点级图自监督学习框架，通过最大化节点级局部视图与全局视图之间的互信息来训练图神经网络编码器——这与以往采用图级全局视图的研究不同。该方法在促进视图间可预测性的同时，对表征的协方差矩阵进行正则化。因此，RGI具有非对比性特征，既不依赖复杂的非对称架构或训练技巧，也无需数据增强和双分支结构。我们在直推式与归纳式两种设置下，使用主流图基准数据集对RGI进行验证，结果表明尽管其结构简单，仍能达到当前最优性能。