The key idea of contrastive learning is to maximize the agreement between views generated from the Graph representation learning (GRL) makes considerable same data instance, while minimizing the agreement between progress recently, which encodes graphs with topological those from different instances. Given its potential in exploiting structures into low-dimensional embeddings. Meanwhile, the data relationships, recent efforts [10, 11, 12] have been time-consuming and costly process of annotating graph labels devoted to advancing contrastive learning to obtain general manually prompts the growth of self-supervised learning graph representations. For example, GraphCL [10] builds (SSL) techniques. As a dominant approach of SSL, Contrastive a graph contrastive learning framework with augmentations learning (CL) learns discriminative representations to learn robust graph representations.