As aforementioned, to mitigate the data insufficiency issue, we i) apply a variational encoder to augment graph-level embeddings in the training phase, and ii) apply a pretraining-assisted multi-task learning paradigm. To demonstrate the rationality of these two strategies, we conduct two ablation studies to analyze their impacts quantitatively in Table . We can find that the removal of these two strategies leads to performance degradation across all the evaluation metrics. Especially, without pretraining, our HGAR model suffers from severe over-fitting issues because of its model size.