Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs for the purpose of learning underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples, or from an external irrelevant graph. However, a significant limitation lies in such strategies, which is the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies. We utilize counterfactual mechanism to produce hard negative samples, which ensures that the generated samples are similar to, but have labels that different from the positive sample. The proposed method achieves satisfying results on several datasets compared to some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conduct some supplementary experiments to give an extensive illustration of the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements.

翻译：图对比学习已成为无监督图表示学习的有力工具。其成功关键在于获取高质量的正负样本作为对比对，以学习输入图的底层结构语义。现有工作通常从同一训练批次中与正样本一起采样负样本，或从外部无关图中采样。然而，此类策略存在显著局限，即不可避免地会采样到假负样本。本文提出一种新颖方法，利用反事实机制为图对比学习生成人工难负样本，即**CGC**，该视角与基于采样的策略截然不同。我们利用反事实机制生成难负样本，确保生成的样本与正样本相似但标签不同。在多个数据集上的实验表明，与若干传统无监督图学习方法及当前最优的图对比学习方法相比，所提方法取得了令人满意的结果。我们还通过补充实验对所提方法进行了全面阐述，包括不同难负样本下CGC的性能，以及基于不同相似度度量生成的难负样本的评估。