  Graph contrastive learning (GCL), standing as the dominant paradigm in the
realm of graph pre-training, has yielded considerable progress. Nonetheless,
its capacity for out-of-distribution (OOD) generalization has been relatively
underexplored. In this work, we point out that the traditional optimization of
InfoNCE in GCL restricts the cross-domain pairs only to be negative samples,
which inevitably enlarges the distribution gap between different domains. This
violates the requirement of domain invariance under OOD scenario and
consequently impairs the model's OOD generalization performance. To address
this issue, we propose a novel strategy "Negative as Positive", where the most
semantically similar cross-domain negative pairs are treated as positive during
GCL. Our experimental results, spanning a wide array of datasets, confirm that
this method substantially improves the OOD generalization performance of GCL.
