图自监督任务的相关性探索
Exploring Correlations of Self-Supervised Tasks for Graphs
AI总结：
针对图自监督学习中任务关联未被探究的问题，该研究通过定义相关值量化任务关联，提出GraphTCM方法增强自监督训练，在多下游任务上性能显著优于现有方法。
摘要：
图自监督学习掀起了无需标注数据即可训练信息丰富表示的研究热潮，但目前学界对图自监督学习的理解仍有限，各类自监督任务之间的内在关联尚未被探究。本文旨在基于任务相关性为图自监督学习提供全新认知。具体而言，我们评估由某一特定任务训练得到的表示在其他任务上的表现，并定义相关值来量化任务相关性。通过该过程，我们揭示了各类自监督任务之间的相关性，且能够衡量其与下游性能密切相关的表达能力。通过分析多个数据集上任务间的相关值，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。为获得性能更强的表示，我们提出Graph Task Correlation Modeling（图任务相关性建模，简称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，我们的方法在多个下游任务上显著优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
