  Graph contrastive learning (GCL) has emerged as a pivotal technique in the
domain of graph representation learning. A crucial aspect of effective GCL is
the caliber of generated positive and negative samples, which is intrinsically
dictated by their resemblance to the original data. Nevertheless, precise
control over similarity during sample generation presents a formidable
challenge, often impeding the effective discovery of representative graph
patterns. To address this challenge, we propose an innovative framework:
Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on
the merits of pair-wise augmentation to engender graph-level positive and
negative samples with controllable similarity, alongside subgraph contrastive
learning to discern effective graph patterns therein. Within the ACGCL
framework, we have devised a novel adversarial curriculum training methodology
that facilitates progressive learning by sequentially increasing the difficulty
of distinguishing the generated samples. Notably, this approach transcends the
prevalent sparsity issue inherent in conventional curriculum learning
strategies by adaptively concentrating on more challenging training data.
Finally, a comprehensive assessment of ACGCL is conducted through extensive
experiments on six well-known benchmark datasets, wherein ACGCL conspicuously
surpasses a set of state-of-the-art baselines.
