The pretasks are mainly built on mutual information estimation, which requires data augmentation to construct positive samples with similar semantics to learn invariant signals and negative samples with dissimilar semantics in order to empower representation discriminability. However, an appropriate data augmentation configuration depends heavily on lots of empirical trials such as choosing the compositions of data augmentation techniques and the corresponding hyperparameter settings. We propose an augmentation-free graph contrastive learning method, invariant-discriminative graph contrastive learning (iGCL), that does not intrinsically require negative samples. iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations. On the one hand, ID loss learns invariant signals by directly minimizing the mean square error between the target samples and positive samples in the representation space. On the other hand, ID loss ensures that the representations are discriminative by an orthonormal constraint forcing the different dimensions of representations to be independent of each other. This prevents representations from collapsing to a point or subspace. Our theoretical analysis explains the effectiveness of ID loss from the perspectives of the redundancy reduction criterion, canonical correlation analysis, and information bottleneck principle. The experimental results demonstrate that iGCL outperforms all baselines on 5 node classification benchmark datasets. iGCL also shows superior performance for different label ratios and is capable of resisting graph attacks, which indicates that iGCL has excellent generalization and robustness. The source code is available at https://github.com/lehaifeng/T-GCN/tree/master/iGCL.

翻译：预训练任务主要基于互信息估计，需要数据增强来构建具有相似语义的正样本以学习不变信号，以及具有不同语义的负样本以增强表示判别性。然而，合适的数据增强配置严重依赖于大量经验性试验，例如选择数据增强技术的组合及相应的超参数设置。我们提出了一种无需数据增强的图对比学习方法——不变-判别图对比学习（iGCL），该方法本质上不需要负样本。iGCL设计了不变-判别损失（ID损失）来学习不变和判别性表示。一方面，ID损失通过直接最小化表示空间中目标样本与正样本之间的均方误差来学习不变信号。另一方面，ID损失通过正交约束强制表示的不同维度彼此独立，从而确保表示的判别性。这防止了表示坍缩到点或子空间。我们的理论分析从冗余减少准则、典型相关分析和信息瓶颈原理的角度解释了ID损失的有效性。实验结果表明，iGCL在5个节点分类基准数据集上优于所有基线方法。iGCL在不同标签比例下也表现出优越性能，并且能够抵抗图攻击，表明iGCL具有优秀的泛化能力和鲁棒性。源代码可在 https://github.com/lehaifeng/T-GCN/tree/master/iGCL 获取。