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.

我们的研究旨在通过任务相关性提供对图自监督学习的新理解，通过评估一个具体任务所训练的表示在其他任务上的性能，定义相关性值来量化任务相关性，并揭示了各种自监督任务之间的任务相关性和其表达能力，进而提出了图任务相关建模（GraphTCM）来增强图自监督训练，并通过实验结果表明我们的方法在各种下游任务上明显优于现有方法。