  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. )
