  Many real-world data can be represented as heterogeneous graphs with
different types of nodes and connections. Heterogeneous graph neural network
model aims to embed nodes or subgraphs into low-dimensional vector space for
various downstream tasks such as node classification, link prediction, etc.
Although several models were proposed recently, they either only aggregate
information from the same type of neighbors, or just indiscriminately treat
homogeneous and heterogeneous neighbors in the same way. Based on these
observations, we propose a new heterogeneous graph neural network model named
HMSG to comprehensively capture structural, semantic and attribute information
from both homogeneous and heterogeneous neighbors. Specifically, we first
decompose the heterogeneous graph into multiple metapath-based homogeneous and
heterogeneous subgraphs, and each subgraph associates specific semantic and
structural information. Then message aggregation methods are applied to each
subgraph independently, so that information can be learned in a more targeted
and efficient manner. Through a type-specific attribute transformation, node
attributes can also be transferred among different types of nodes. Finally, we
fuse information from subgraphs together to get the complete representation.
Extensive experiments on several datasets for node classification, node
clustering and link prediction tasks show that HMSG achieves the best
performance in all evaluation metrics than state-of-the-art baselines.
