Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in real-world applications, Graph Contrastive Learning (GCL) is leveraged to train GNNs with limited or even no labels by maximizing the mutual information between nodes in its augmented views generated from the original graph. However, the distribution of graphs remains unconsidered in view generation, resulting in the ignorance of unseen edges in most existing literature, which is empirically shown to be able to improve GCL's performance in our experiments. To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning. GACN develops a view generator and a view discriminator to generate augmented views automatically in an adversarial style. Then, GACN leverages these views to train a GNN encoder with two carefully designed self-supervised learning losses, including the graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore, we design an optimization framework to train all GACN modules jointly. Extensive experiments on seven real-world datasets show that GACN is able to generate high-quality augmented views for GCL and is superior to twelve state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly discovers that the generated views in data augmentation finally conform to the well-known preferential attachment rule in online networks.

翻译：图神经网络（GNNs）通过有监督的端到端训练，在利用节点表示完成许多下游任务方面展现出显著效果。为解决现实应用中普遍存在的标签稀缺问题，图对比学习（GCL）通过最大化原始图生成的增强视图中节点间的互信息，能够在有限甚至无标签条件下训练GNNs。然而现有文献在视图生成过程中普遍未考虑图的分布特性，导致对未观测边的忽视——我们的实验表明这类边能有效提升GCL性能。为此，我们提出融合图生成对抗网络（GANs）来学习GCL的视图分布，以期：i）自动捕获图特征以生成增强视图，ii）联合训练图GAN模型与GCL模型。具体而言，我们提出GACN——一种面向图表示学习的生成对抗对比学习网络。GACN通过视图生成器与视图判别器以对抗方式自动生成增强视图，进而利用这些视图训练GNN编码器，并设计两种自监督学习损失：图对比损失与贝叶斯个性化排序损失。此外，我们构建了联合优化框架用于训练全部GACN模块。在七个真实数据集上的大量实验表明，GACN能够为GCL生成高质量的增强视图，且性能优于十二种最先进基线方法。值得注意的是，我们提出的GACN意外发现：数据增强过程中生成的视图最终符合在线网络中著名的优先连接规则。