  Representation learning on heterogeneous graphs aims to obtain meaningful
node representations to facilitate various downstream tasks, such as node
classification and link prediction. Existing heterogeneous graph learning
methods are primarily developed by following the propagation mechanism of node
representations. There are few efforts on studying the role of relations for
improving the learning of more fine-grained node representations. Indeed, it is
important to collaboratively learn the semantic representations of relations
and discern node representations with respect to different relation types. To
this end, in this paper, we propose a novel Relation-aware Heterogeneous Graph
Neural Network, namely R-HGNN, to learn node representations on heterogeneous
graphs at a fine-grained level by considering relation-aware characteristics.
Specifically, a dedicated graph convolution component is first designed to
learn unique node representations from each relation-specific graph separately.
Then, a cross-relation message passing module is developed to improve the
interactions of node representations across different relations. Also, the
relation representations are learned in a layer-wise manner to capture relation
semantics, which are used to guide the node representation learning process.
Moreover, a semantic fusing module is presented to aggregate relation-aware
node representations into a compact representation with the learned relation
representations. Finally, we conduct extensive experiments on a variety of
graph learning tasks, and experimental results demonstrate that our approach
consistently outperforms existing methods among all the tasks.
