Graph Neural Networks (GNNs) have exhibited significant success in various applications, but they face challenges when labeled nodes are limited. A novel self-supervised learning paradigm has emerged, enabling GNN training without labeled nodes and even surpassing GNNs with limited labeled data. However, self-supervised methods lack class-discriminative node representations due to the absence of labeled information during training. In this paper, we exploit a supervised graph contrastive learning approach (SGCL) framework to tackle the issue of limited labeled nodes, ensuring coherent grouping of nodes within the same class. We propose augmentation techniques based on a novel centrality function to highlight important topological structures. Additionally, we introduce a supervised contrastive learning method that removes the necessity for negative samples and simplifies complex elements effortlessly. Our approach combines supervised contrastive loss and node similarity regularization while achieving consistent grouping of unlabeled nodes with labeled ones. Furthermore, we utilize the pseudo-labeling technique to propagate label information to distant nodes and address the underfitting problem, especially with low-degree nodes. Experimental results on real-world graphs demonstrate that SGCL outperforms both semi-supervised and self-supervised methods in node classification.

中文翻译：

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用于图表示增强的监督对比学习

图神经网络（GNN）在各种应用中取得了巨大的成功，但当标记节点有限时，它们面临着挑战。一种新颖的自监督学习范式已经出现，使得无需标记节点的 GNN 训练成为可能，甚至超越了带有有限标记数据的 GNN。然而，由于训练期间缺乏标记信息，自监督方法缺乏类判别节点表示。在本文中，我们利用监督图对比学习方法（SGCL）框架来解决有限标记节点的问题，确保同一类内节点的连贯分组。我们提出基于新颖的中心性函数的增强技术来突出重要的拓扑结构。此外，我们引入了一种监督对比学习方法，消除了负样本的必要性并毫不费力地简化了复杂的元素。我们的方法结合了监督对比损失和节点相似性正则化，同时实现未标记节点与标记节点的一致分组。此外，我们利用伪标签技术将标签信息传播到远处的节点并解决欠拟合问题，特别是对于低度节点。真实世界图上的实验结果表明，SGCL 在节点分类方面优于半监督和自监督方法。