Graph classification is a widely studied problem and has broad applications. In many real-world problems, the number of labeled graphs available for training models limited, which renders these prone to overfitting. To address this problem, we propose two approaches based on contrastive self-supervised learning (CSSL) alleviate first approach, use CSSL pretrain graph encoders widely-available u...