Contrastive learning has actively been used for unsupervised graph representation learning owing to its success in computer vision. Most graph contrastive learning methods use instance discrimination. It treats each instance as a distinct class against a query instance as the pretext task. However, such methods inevitably cause a class collision problem because some instances may belong to the same class as the query. Thus, the similarity shared through instances from the same class cannot be reflected in the pre-training stage. To address this problem, we propose graph contrastive learning with consistency regularization (GCCR), which introduces a consistency regularization term to graph contrastive learning. Unlike existing methods, GCCR can obtain a graph representation that reflects intra-class similarity by introducing a consistency regularization term. To verify the effectiveness of the proposed method, we performed extensive experiments and demonstrated that GCCR improved the quality of graph representations for most datasets. Notably, experimental results in various settings show that the proposed method can learn effective graph representations with better robustness against transformations than other state-of-the-art methods.

中文翻译：

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具有一致性正则化的图对比学习

由于对比学习在计算机视觉领域的成功，它已被积极用于无监督图表示学习。大多数图对比学习方法都使用实例辨别。它将每个实例视为一个不同的类，针对作为借口任务的查询实例。然而，这样的方法不可避免地会导致类冲突问题，因为某些实例可能与查询属于同一类。因此，来自同一类的实例共享的相似性无法在预训练阶段反映出来。为了解决这个问题，我们提出了带有一致性正则化的图对比学习（GCCR），它在图对比学习中引入了一致性正则化项。与现有方法不同，GCCR 可以通过引入一致性正则化项来获得反映类内相似性的图表示。为了验证所提出方法的有效性，我们进行了广泛的实验，并证明 GCCR 提高了大多数数据集的图形表示质量。值得注意的是，各种设置下的实验结果表明，所提出的方法可以学习有效的图表示，并且比其他最先进的方法对变换具有更好的鲁棒性。