The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling. Despite the great success of SSL methods in computer vision and natural language processing, most of them employ contrastive learning objectives that require negative samples, which are hard to define. This becomes even more challenging in the case of graphs and is a bottleneck for achieving robust representations. To overcome such limitations, we propose a framework for self-supervised graph representation learning - Graph Barlow Twins, which utilizes a cross-correlation-based loss function instead of negative samples. Moreover, it does not rely on non-symmetric neural network architectures - in contrast to state-of-the-art self-supervised graph representation learning method BGRL. We show that our method achieves as competitive results as the best self-supervised methods and fully supervised ones while requiring fewer hyperparameters and substantially shorter computation time (ca. 30 times faster than BGRL).

翻译：自我监督学习范式是重要的探索领域，旨在消除昂贵的数据标注需求。尽管自我监督学习方法在计算机视觉和自然语言处理领域取得了巨大成功，但多数方法依赖需要负样本的对比学习目标，而负样本本身难以定义。在图数据场景下，这一挑战尤为突出，成为实现鲁棒表示的瓶颈。为克服此类局限，我们提出了一种基于图的自我监督表示学习框架——Graph Barlow Twins，该框架利用基于互相关性的损失函数替代负样本。此外，与当前最先进的自我监督图表示学习方法BGRL不同，本方法不依赖非对称神经网络架构。实验表明，我们的方法在仅需更少超参数且计算时间大幅缩短（比BGRL快约30倍）的前提下，能够取得与最优自我监督方法及全监督方法相当的竞争性结果。