A Theory

In this section, we provide more details of model implementation and experiment setup for reproducibility of the experimental results. The prediction model uses negative log likelihood loss. As shown in Table 2, we observe that the prediction model f achieves high performance of graph classification on all datasets. CLEAR is designed in a general way, which can be adaptable to different graph representation learning modules and different techniques in graph generative models. B.2 Details of Experiment Setup B.2.1 Baseline Settings Here we introduce more details of baseline setting: In each step, at most one edge can be inserted or removed.