In this paper, we focus on graph representation learning of heterogeneous
information network (HIN), in which various types of vertices are connected by
various types of relations. Most of the existing methods conducted on HIN
revise homogeneous graph embedding models via meta-paths to learn
low-dimensional vector space of HIN. In this paper, we propose a novel
Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly
encode structural information of HIN without meta-path and achieve more
informative representations. With this method, domain experts will not be
needed to design meta-path schemes and the heterogeneous information can be
processed automatically by our proposed model. Specifically, we implicitly
represent heterogeneous information using the following two methods: 1) we
model the transformation between heterogeneous vertices through a projection in
low-dimensional entity spaces; 2) afterwards, we apply the graph neural network
to aggregate multi-relational information of projected neighborhood by means of
attention mechanism. We also present three extensions of HetSANN, i.e.,
voices-sharing product attention for the pairwise relationships in HIN,
cycle-consistency loss to retain the transformation between heterogeneous
entity spaces, and multi-task learning with full use of information. The
experiments conducted on three public datasets demonstrate that our proposed
models achieve significant and consistent improvements compared to
state-of-the-art solutions.

本文针对异构信息网络（HIN）的图表示学习进行了研究，并提出了一种新颖的基于异构图结构注意力神经网络（HetSANN）的方法，该方法在不需要领域专家设计元路径方案的情况下能够自动处理异构信息。实验证明，该方法相较于现有的 HIN 嵌入模型有显著和一致的提升。