  Graph neural networks for heterogeneous graph embedding is to project nodes
into a low-dimensional space by exploring the heterogeneity and semantics of
the heterogeneous graph. However, on the one hand, most of existing
heterogeneous graph embedding methods either insufficiently model the local
structure under specific semantic, or neglect the heterogeneity when
aggregating information from it. On the other hand, representations from
multiple semantics are not comprehensively integrated to obtain versatile node
embeddings. To address the problem, we propose a Heterogeneous Graph Neural
Network with Multi-View Representation Learning (named MV-HetGNN) for
heterogeneous graph embedding by introducing the idea of multi-view
representation learning. The proposed model consists of node feature
transformation, view-specific ego graph encoding and auto multi-view fusion to
thoroughly learn complex structural and semantic information for generating
comprehensive node representations. Extensive experiments on three real-world
heterogeneous graph datasets show that the proposed MV-HetGNN model
consistently outperforms all the state-of-the-art GNN baselines in various
downstream tasks, e.g., node classification, node clustering, and link
prediction.
