  Graph classification is a widely studied problem and has broad applications.
In many real-world problems, the number of labeled graphs available for
training classification models is limited, which renders these models prone to
overfitting. To address this problem, we propose two approaches based on
contrastive self-supervised learning (CSSL) to alleviate overfitting. In the
first approach, we use CSSL to pretrain graph encoders on widely-available
unlabeled graphs without relying on human-provided labels, then finetune the
pretrained encoders on labeled graphs. In the second approach, we develop a
regularizer based on CSSL, and solve the supervised classification task and the
unsupervised CSSL task simultaneously. To perform CSSL on graphs, given a
collection of original graphs, we perform data augmentation to create augmented
graphs out of the original graphs. An augmented graph is created by
consecutively applying a sequence of graph alteration operations. A contrastive
loss is defined to learn graph encoders by judging whether two augmented graphs
are from the same original graph. Experiments on various graph classification
datasets demonstrate the effectiveness of our proposed methods.
