Graph neural network, as a powerful graph representation technique based on
deep learning, has shown superior performance and attracted considerable
research interest. However, it has not been fully considered in graph neural
network for heterogeneous graph which contains different types of nodes and
links. The heterogeneity and rich semantic information bring great challenges
for designing a graph neural network for heterogeneous graph. Recently, one of
the most exciting advancements in deep learning is the attention mechanism,
whose great potential has been well demonstrated in various areas. In this
paper, we first propose a novel heterogeneous graph neural network based on the
hierarchical attention, including node-level and semantic-level attentions.
Specifically, the node-level attention aims to learn the importance between a
node and its metapath based neighbors, while the semantic-level attention is
able to learn the importance of different meta-paths. With the learned
importance from both node-level and semantic-level attention, the importance of
node and meta-path can be fully considered. Then the proposed model can
generate node embedding by aggregating features from meta-path based neighbors
in a hierarchical manner. Extensive experimental results on three real-world
heterogeneous graphs not only show the superior performance of our proposed
model over the state-of-the-arts, but also demonstrate its potentially good
interpretability for graph analysis.

该论文提出了一种基于分层注意力机制的异构图神经网络，包括节点层次和语义层次的注意力机制，通过从元路径邻居中汇总特征来生成节点嵌入，其在三个真实的异构图上表现出优越性能且有助于图分析。