Graph self-supervised learning has sparked a research surge in training informative representations without accessing any labeled data. However, our understanding of graph self-supervised learning remains limited, and the inherent relationships between various self-supervised tasks are still unexplored. Our paper aims to provide a fresh understanding of graph self-supervised learning based on task correlations. Specifically, we evaluate the performance of the representations trained by one specific task on other tasks and define correlation values to quantify task correlations. Through this process, we unveil the task correlations between various self-supervised tasks and can measure their expressive capabilities, which are closely related to downstream performance. By analyzing the correlation values between tasks across various datasets, we reveal the complexity of task correlations and the limitations of existing multi-task learning methods. To obtain more capable representations, we propose Graph Task Correlation Modeling (GraphTCM) to illustrate the task correlations and utilize it to enhance graph self-supervised training. The experimental results indicate that our method significantly outperforms existing methods across various downstream tasks.

翻译：图自监督学习在无需标注数据的情况下训练信息丰富的表示，引发了研究热潮。然而，我们对图自监督学习的理解仍然有限，各种自监督任务之间的内在关系尚未得到探索。本文旨在基于任务相关性提供对图自监督学习的新理解。具体而言，我们评估由特定任务训练的表示在其他任务上的表现，并定义相关性值以量化任务相关性。通过这一过程，我们揭示了各种自监督任务之间的相关性，并可以衡量它们的表达能力，这直接关系到下游任务的性能。通过分析不同数据集上任务之间的相关性值，我们揭示了任务相关性的复杂性以及现有多任务学习方法的局限性。为了获得更具能力的表示，我们提出图任务相关性建模（GraphTCM）来阐释任务相关性，并利用其增强图自监督训练。实验结果表明，我们的方法在各种下游任务中显著优于现有方法。