  Existing graph contrastive learning methods rely on augmentation techniques
based on random perturbations (e.g., randomly adding or dropping edges and
nodes). Nevertheless, altering certain edges or nodes can unexpectedly change
the graph characteristics, and choosing the optimal perturbing ratio for each
dataset requires onerous manual tuning. In this paper, we introduce Implicit
Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent
space learned from a Variational Graph Auto-Encoder by reconstructing graph
topological structure. Importantly, instead of explicitly sampling
augmentations from latent distributions, we further propose an upper bound for
the expected contrastive loss to improve the efficiency of our learning
algorithm. Thus, graph semantics can be preserved within the augmentations in
an intelligent way without arbitrary manual design or prior human knowledge.
Experimental results on both graph-level and node-level tasks show that the
proposed method achieves state-of-the-art performance compared to other
benchmarks, where ablation studies in the end demonstrate the effectiveness of
modules in iGCL.
