  Graph contrastive learning (GCL) is a popular method for leaning graph
representations by maximizing the consistency of features across augmented
views. Traditional GCL methods utilize single-perspective i.e. data or
model-perspective) augmentation to generate positive samples, restraining the
diversity of positive samples. In addition, these positive samples may be
unreliable due to uncontrollable augmentation strategies that potentially alter
the semantic information. To address these challenges, this paper proposed a
innovative framework termed dual-perspective cross graph contrastive learning
(DC-GCL), which incorporates three modifications designed to enhance positive
sample diversity and reliability: 1) We propose dual-perspective augmentation
strategy that provide the model with more diverse training data, enabling the
model effective learning of feature consistency across different views. 2) From
the data perspective, we slightly perturb the original graphs using
controllable data augmentation, effectively preserving their semantic
information. 3) From the model perspective, we enhance the encoder by utilizing
more powerful graph transformers instead of graph neural networks. Based on the
model's architecture, we propose three pruning-based strategies to slightly
perturb the encoder, providing more reliable positive samples. These
modifications collectively form the DC-GCL's foundation and provide more
diverse and reliable training inputs, offering significant improvements over
traditional GCL methods. Extensive experiments on various benchmarks
demonstrate that DC-GCL consistently outperforms different baselines on various
datasets and tasks.
