  Heterogeneous graphs are pervasive in practical scenarios, where each graph
consists of multiple types of nodes and edges. Representation learning on
heterogeneous graphs aims to obtain low-dimensional node representations that
could preserve both node attributes and relation information. However, most of
the existing graph convolution approaches were designed for homogeneous graphs,
and therefore cannot handle heterogeneous graphs. Some recent methods designed
for heterogeneous graphs are also faced with several issues, including the
insufficient utilization of heterogeneous properties, structural information
loss, and lack of interpretability. In this paper, we propose HGConv, a novel
Heterogeneous Graph Convolution approach, to learn comprehensive node
representations on heterogeneous graphs with a hybrid micro/macro level
convolutional operation. Different from existing methods, HGConv could perform
convolutions on the intrinsic structure of heterogeneous graphs directly at
both micro and macro levels: A micro-level convolution to learn the importance
of nodes within the same relation, and a macro-level convolution to distinguish
the subtle difference across different relations. The hybrid strategy enables
HGConv to fully leverage heterogeneous information with proper
interpretability. Moreover, a weighted residual connection is designed to
aggregate both inherent attributes and neighbor information of the focal node
adaptively. Extensive experiments on various tasks demonstrate not only the
superiority of HGConv over existing methods, but also the intuitive
interpretability of our approach for graph analysis.
