Gaussian Mutual Information Maximization for Efficient Graph Self-Supervised Learning: Bridging Contrastive-based to Decorrelation-based

Enlightened by the InfoMax principle, Graph Contrastive Learning (GCL) has achieved remarkable performance in processing large amounts of unlabeled graph data. Due to the impracticality of precisely calculating mutual information (MI), conventional contrastive learning methods turn to approximate its lower bound using parametric neural estimators, which inevitably introduces additional parameters and leads to increased computational complexity. Building upon a common Gaussian assumption on the distribution of node representations, a computationally tractable surrogate for the original MI can be rigorously derived, termed as Gaussian Mutual Information (GMI). Leveraging multi-view priors of GCL, we induce an efficient contrastive objective based on GMI with performance guarantees, eliminating the reliance on parameterized estimators and negative samples. The emergence of another decorrelation-based self-supervised learning branch parallels contrastive-based approaches. By positioning the proposed GMI-based objective as a pivot, we bridge the gap between these two research areas from two aspects of approximate form and consistent solution, which contributes to the advancement of a unified theoretical framework for self-supervised learning. Extensive comparison experiments, ablation studies, and visual analysis provide compelling evidence for the effectiveness and efficiency of our method while supporting our theoretical achievements.