  Graph contrastive learning (GCL) shows great potential in unsupervised graph
representation learning. Data augmentation plays a vital role in GCL, and its
optimal choice heavily depends on the downstream task. Many GCL methods with
automated data augmentation face the risk of insufficient information as they
fail to preserve the essential information necessary for the downstream task.
To solve this problem, we propose InfoMin-Max for automated Graph contrastive
learning (GIMM), which prevents GCL from encoding redundant information and
losing essential information. GIMM consists of two major modules: (1) automated
graph view generator, which acquires the approximation of InfoMin's optimal
views through adversarial training without requiring task-relevant information;
(2) view comparison, which learns an excellent encoder by applying InfoMax to
view representations. To the best of our knowledge, GIMM is the first method
that combines the InfoMin and InfoMax principles in GCL. Besides, GIMM
introduces randomness to augmentation, thus stabilizing the model against
perturbations. Extensive experiments on unsupervised and semi-supervised
learning for node and graph classification demonstrate the superiority of our
GIMM over state-of-the-art GCL methods with automated and manual data
augmentation.
