Contrastive learning (CL), which can extract the information shared between different contrastive views, has become a popular paradigm for vision representation learning. Inspired by the success in computer vision, recent work introduces CL into graph modeling, dubbed as graph contrastive learning (GCL). However, generating contrastive views in graphs is more challenging than that in images, since we have little prior knowledge on how to significantly augment a graph without changing its labels. We argue that typical data augmentation techniques (e.g., edge dropping) in GCL cannot generate diverse enough contrastive views to filter out noises. Moreover, previous GCL methods employ two view encoders with exactly the same neural architecture and tied parameters, which further harms the diversity of augmented views. To address this limitation, we propose a novel paradigm named model augmented GCL (MA-GCL), which will focus on manipulating the architectures of view encoders instead of perturbing graph inputs. Specifically, we present three easy-to-implement model augmentation tricks for GCL, namely asymmetric, random and shuffling, which can respectively help alleviate high- frequency noises, enrich training instances and bring safer augmentations. All three tricks are compatible with typical data augmentations. Experimental results show that MA-GCL can achieve state-of-the-art performance on node classification benchmarks by applying the three tricks on a simple base model. Extensive studies also validate our motivation and the effectiveness of each trick. (Code, data and appendix are available at https://github.com/GXM1141/MA-GCL. )

翻译：对比性学习(CL)可以解析不同对比观点之间共享的信息,它已成为视觉代表学习的流行范例。在计算机视觉的成功激励下,最近的工作将CL引入图形模型模型,称为图形对比性学习(GCL )。然而,在图形中产生对比性观点比图像中更具挑战性,因为我们对如何在不改变其标签的情况下大幅增高图表缺乏先前的知识。我们争辩说,GCL的典型数据增强技术(例如,边缘下降)不能产生足够多样的对比性观点,以过滤噪音。此外,以前的GCL方法使用两种视图编码器,其神经结构与附加参数完全相同,从而进一步损害扩大观点的多样性。然而,为了解决这一局限性,我们提出了一种名为模型增强的GL(MA-GC)的新模式,其重点是如何在不改变其标签标签的情况下对图像结构进行操纵。我们为GCL提供了三种简单易执行的增强模型技巧,即不对称的、随机的和抖动的模型,它可以分别帮助降低高频度的螺旋结构结构结构和绑定的参数,同时在高频度的精确度的精确度模型上进行三度的ML的精确度分析。 使A-G-A-A-A-A-A-A-A-A-A-A-A-A-A-S-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-