  Graph Neural Networks (GNNs) are widely used in graph representation
learning. However, most GNN methods are designed for either homogeneous or
heterogeneous graphs. In this paper, we propose a new model, Hop-Hop
Relation-aware Graph Neural Network (HHR-GNN), to unify representation learning
for these two types of graphs. HHR-GNN learns a personalized receptive field
for each node by leveraging knowledge graph embedding to learn relation scores
between the central node's representations at different hops. In neighborhood
aggregation, our model simultaneously allows for hop-aware projection and
aggregation. This mechanism enables the central node to learn a hop-wise
neighborhood mixing that can be applied to both homogeneous and heterogeneous
graphs. Experimental results on five benchmarks show the competitive
performance of our model compared to state-of-the-art GNNs, e.g., up to 13K
faster in terms of time cost per training epoch on large heterogeneous graphs.
