GPS: Graph Contrastive Learning via Multi-scale Augmented Views from
  Adversarial Pooling
Wei Ju Yiyang Gu Zhengyang Mao Ziyue Qiao Yifang Qin Xiao Luo Hui Xiong Ming Zhang
Self-supervised graph representation learning has recently shown considerable
promise in a range of fields, including bioinformatics and social networks. A
large number of graph contrastive learning approaches have shown promising
performance for representation learning on graphs, which train models by
maximizing agreement between original graphs and their augmented views (i.e.,
positive views). Unfortunately, these methods usually involve pre-defined
augmentation strategies based on the knowledge of human experts. Moreover,
these strategies may fail to generate challenging positive views to provide
sufficient supervision signals. In this paper, we present a novel approach
named Graph Pooling ContraSt (GPS) to address these issues. Motivated by the
fact that graph pooling can adaptively coarsen the graph with the removal of
redundancy, we rethink graph pooling and leverage it to automatically generate
multi-scale positive views with varying emphasis on providing challenging
positives and preserving semantics, i.e., strongly-augmented view and
weakly-augmented view. Then, we incorporate both views into a joint contrastive
learning framework with similarity learning and consistency learning, where our
pooling module is adversarially trained with respect to the encoder for
adversarial robustness. Experiments on twelve datasets on both graph
classification and transfer learning tasks verify the superiority of the
proposed method over its counterparts.