Ablation study for TAG.

We report accuracies of graph classification using SVM and MLP classifiers. Bold, underlined, and Avg. texts denote the best, the second-best, and the average accuracy, respectively. The methods w/o curriculum and w/o node-level refer to TAG without the curriculum learning and the node-level contrastive learning, respectively. The fixed augmentation methods (Edit feature + Delete node, Edit feature + Delete edge, etc.) run TAG by using the same feature and structure augmentations for all graphs, while TAG randomly selects an augmentation for each graph. Note that TAG shows the best performance for all cases.