Review for NeurIPS paper: Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

SELAR introduces auxiliary tasks (i.e., metapath prediction) to augment main task and learn better representations. A Hint network is further proposed for better optimization. Experiments on several datasets demonstrate that the proposed model outperforms some baseline methods for node classification and link prediction. Pros 1 The problem is important.