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:对比图的学习已成为一个强大的工具,可以用来进行未经监督的图形代表学学习。图表对比学习的成功关键在于获取高质量的正反样本,作为对比对应的对等样本,以学习输入图的基本结构语义。最近的工作通常用正反样或外部无关的图表对来自同一培训批次的负面样本进行抽样抽样抽样,但是,这种战略存在很大的局限性,这是抽样假反样的不可避免的问题。在本文中,我们提出了一个使用\ textbf{C}反现实机制的新方法,以生成人造硬反样本,用于为\ textbf{G}raph\ textbf{C}进行对比性学习,即\ textbf{CGC},该方法与这些基于抽样的战略有着不同的观点。我们利用反现实机制来制作硬反向的样本,这确保所生成的样本与正样样本相似,但有不同于正样样本的标签。与一些传统的未经监督的硬性图表学习方法相比,我们取得了满意的结果。我们用一些传统的不可靠的硬性图表学习方法,并用一些SOTA对比图样的模型进行不同的硬性分析研究。