Abstract
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.
Abstract (translated)
图形自监督学习引起了在无需访问任何标注数据的情况下进行训练的有用表示的研究爆发。然而，我们对图形自监督学习的理解仍然有限，并且各种自监督任务之间的内在关系仍然没有被探索。本文旨在基于任务相关性提供对图形自监督学习的全新理解。具体来说，我们评估特定任务对其他任务的表示性能，并定义关联值来量化任务相关性。通过这个过程，我们揭示了各种自监督任务之间的任务相关性，并可以衡量它们的表达能力，这与下游性能密切相关。通过分析各种数据集之间任务之间的关联值，我们揭示了任务相关性的复杂性和现有多任务学习方法的局限性。为了获得更强大的表示，我们提出了Graph Task Correlation Modeling (GraphTCM)来说明任务相关性，并利用它来增强图形自监督训练。实验结果表明，我们的方法在各种下游任务上显著优于现有方法。
URL
https://arxiv.org/abs/2405.04245
