  Most real-world datasets are inherently heterogeneous graphs, which involve a
diversity of node and relation types. Heterogeneous graph embedding is to learn
the structure and semantic information from the graph, and then embed it into
the low-dimensional node representation. Existing methods usually capture the
composite relation of a heterogeneous graph by defining metapath, which
represent a semantic of the graph. However, these methods either ignore node
attributes, or discard the local and global information of the graph, or only
consider one metapath. To address these limitations, we propose a
Metapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network(MHN)
to improve performance. Specially, MHN employs node base embedding to
encapsulate node attributes, BFS and DFS neighbors aggregation within a
metapath to capture local and global information, and metapaths aggregation to
combine different semantics of the heterogeneous graph. We conduct extensive
experiments for the proposed MHN on three real-world heterogeneous graph
datasets, including node classification, link prediction and online A/B test on
Alibaba mobile application. Results demonstrate that MHN performs better than
other state-of-the-art baselines.
