We thank all four reviewers for unanimous support for the paper and constructive comments. Overall, reviewers are positive about our contributions: [R5] "The proposed framework is very general and It is the'first' paper to do so.", The overall quality of this paper is good." Is the proposed method applicable to existing heterogeneous GNNs such as GTNs [1]? Y es, as [R4] pointed out, our framework can be applied to any GNNs in a plug-in manner.

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.

Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the representation can be further improved by auxiliary tasks. However, the auxiliary tasks for heterogeneous graphs, which contain rich semantic information with various types of nodes and edges, have less explored in the literature. In this paper, to learn graph neural networks on heterogeneous graphs we propose a novel self-supervised auxiliary learning method using meta-paths, which are composite relations of multiple edge types. Our proposed method is learning to learn a primary task by predicting meta-paths as auxiliary tasks. This can be viewed as a type of meta-learning. The proposed method can identify an effective combination of auxiliary tasks and automatically balance them to improve the primary task. Our methods can be applied to any graph neural networks in a plug-in manner without manual labeling or additional data. The experiments demonstrate that the proposed method consistently improves the performance of link prediction and node classification on heterogeneous graphs.