  Among different existing graph self-supervised learning strategies, graph
contrastive learning (GCL) has been one of the most prevalent approaches to
this problem. Despite the remarkable performance those GCL methods have
achieved, existing GCL methods that heavily depend on various manually designed
augmentation techniques still struggle to alleviate the feature suppression
issue without risking losing task-relevant information. Consequently, the
learned representation is either brittle or unilluminating. In light of this,
we introduce the Graph Contrastive Learning with Cross-View Reconstruction
(GraphCV), which follows the information bottleneck principle to learn minimal
yet sufficient representation from graph data. Specifically, GraphCV aims to
elicit the predictive (useful for downstream instance discrimination) and other
non-predictive features separately. Except for the conventional contrastive
loss which guarantees the consistency and sufficiency of the representation
across different augmentation views, we introduce a cross-view reconstruction
mechanism to pursue the disentanglement of the two learned representations.
Besides, an adversarial view perturbed from the original view is added as the
third view for the contrastive loss to guarantee the intactness of the global
semantics and improve the representation robustness. We empirically demonstrate
that our proposed model outperforms the state-of-the-art on graph
classification task over multiple benchmark datasets.
