  Graph contrastive learning has emerged as a powerful tool for unsupervised
graph representation learning. The key to the success of graph contrastive
learning is to acquire high-quality positive and negative samples as
contrasting pairs for the purpose of learning underlying structural semantics
of the input graph. Recent works usually sample negative samples from the same
training batch with the positive samples, or from an external irrelevant graph.
However, a significant limitation lies in such strategies, which is the
unavoidable problem of sampling false negative samples. In this paper, we
propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate
artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive
learning, namely \textbf{CGC}, which has a different perspective compared to
those sampling-based strategies. We utilize counterfactual mechanism to produce
hard negative samples, which ensures that the generated samples are similar to,
but have labels that different from the positive sample. The proposed method
achieves satisfying results on several datasets compared to some traditional
unsupervised graph learning methods and some SOTA graph contrastive learning
methods. We also conduct some supplementary experiments to give an extensive
illustration of the proposed method, including the performances of CGC with
different hard negative samples and evaluations for hard negative samples
generated with different similarity measurements.
