Graph Contrastive Learning with Graph Info-Min

The complexity of the graph structure poses a challenge for graph representation learning. Contrastive learning offers a straightforward and efficient unsupervised framework for graph representation learning. It achieves unsupervised learning by augmenting the original views and comparing them with the augmented views. Several methods based on this framework have achieved significant progress in the field of graph representation learning. Despite its success, the factors contributing to good augmented views in graph contrast learning have received less attention. In order to address this issue, we introduce the graph info-min principle. We investigate the relationship between mutual information (MI) and good augmented views through experimental and theoretical analysis. Additionally, we present a new contrastive learning method called Info-min Contrastive Learning (IMCL). Specifically, The method comprises an adaptive graph augmentation generator and a pseudo-label generator. The graph augmentation generator ensures sufficient differentiation between the augmented and original views. The pseudo-label generator generates pseudo-labels as supervision signals, ensuring consistency between the classification results of augmented views and original views. Our method demonstrates excellent performance through extensive experimental results on various datasets.