  In recent years, graph neural networks (GNNs) have been widely adopted in the
representation learning of graph-structured data and provided state-of-the-art
performance in various applications such as link prediction, node
classification, and recommendation. Motivated by recent advances of
self-supervision for representation learning in natural language processing and
computer vision, self-supervised learning has been recently studied to leverage
unlabeled graph-structured data. However, employing self-supervision tasks as
auxiliary tasks to assist a primary task has been less explored in the
literature on graphs. In this paper, we propose a novel self-supervised
auxiliary learning framework to effectively learn graph neural networks.
Moreover, this work is the first study showing that a meta-path prediction is
beneficial as a self-supervised auxiliary task for heterogeneous graphs. Our
method is learning to learn a primary task with various auxiliary tasks to
improve generalization performance. The proposed method identifies an effective
combination of auxiliary tasks and automatically balances them to improve the
primary task. Our methods can be applied to any graph neural network in a
plug-in manner without manual labeling or additional data. Also, it can be
extended to any other auxiliary tasks. Our experiments demonstrate that the
proposed method consistently improves the performance of node classification
and link prediction.
