Real world data is mostly unlabeled or only few instances are labeled. Manually labeling data is a very expensive and daunting task. This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-supervised/supervised techniques. Contrastive self-supervised learning has emerged as a powerful direction, in some cases outperforming supervised techniques. In this study, we propose, SelfGNN, a novel contrastive self-supervised graph neural network (GNN) without relying on explicit contrastive terms. We leverage Batch Normalization, which introduces implicit contrastive terms, without sacrificing performance. Furthermore, as data augmentation is key in contrastive learning, we introduce four feature augmentation (FA) techniques for graphs. Though graph topological augmentation (TA) is commonly used, our empirical findings show that FA perform as good as TA. Moreover, FA incurs no computational overhead, unlike TA, which often has O(N^3) time complexity, N-number of nodes. Our empirical evaluation on seven publicly available real-world data shows that, SelfGNN is powerful and leads to a performance comparable with SOTA supervised GNNs and always better than SOTA semi-supervised and unsupervised GNNs. The source code is available at https://github.com/zekarias-tilahun/SelfGNN.

本研究提出了SelfGNN，它是一种基于对比自监督技术的图神经网络，采用批量标准化和四种图形特征增强技术来实现无监督学习。除了使用常用的图形拓扑增强技术（TA），实验证明我们提出的特征增强（FA）能够与TA同样好地发挥作用，而且没有计算开销。在七种公开数据集上的实验结果表明，SelfGNN表现出较高水平的性能，与SOTA监督GNN性能相当，并且始终优于SOTA半监督和无监督GNN。

无需显式负采样的自监督图神经网络