  Unsupervised graph representation learning is critical to a wide range of
applications where labels may be scarce or expensive to procure. Contrastive
learning (CL) is an increasingly popular paradigm for such settings and the
state-of-the-art in unsupervised visual representation learning. Recent work
attributes the success of visual CL to use of task-relevant augmentations and
large, diverse datasets. Interestingly, graph CL frameworks report strong
performance despite using orders of magnitude smaller datasets and employing
domain-agnostic graph augmentations (DAGAs). Motivated by this discrepancy, we
probe the quality of representations learnt by popular graph CL frameworks
using DAGAs. We find that DAGAs can destroy task-relevant information and harm
the model's ability to learn discriminative representations. On small benchmark
datasets, we show the inductive bias of graph neural networks can significantly
compensate for this weak discriminability. Based on our findings, we propose
several sanity checks that enable practitioners to quickly assess the quality
of their model's learned representations. We further propose a broad strategy
for designing task-aware augmentations that are amenable to graph CL and
demonstrate its efficacy on two large-scale, complex graph applications. For
example, in graph-based document classification, we show task-aware
augmentations improve accuracy up to 20%.
