Exploiting Latent Attribute Interaction with Transformer on
  Heterogeneous Information Networks
Zeyuan Zhao Qingqing Ge Anfeng Cheng Yiding Liu Xiang Li Shuaiqiang Wang
Heterogeneous graph neural networks (HGNNs) have recently shown impressive
capability in modeling heterogeneous graphs that are ubiquitous in real-world
applications. Due to the diversity of attributes of nodes in different types,
most existing models first align nodes by mapping them into the same
low-dimensional space. However, in this way, they lose the type information of
nodes. In addition, most of them only consider the interactions between nodes
while neglecting the high-order information behind the latent interactions
among different node features. To address these problems, in this paper, we
propose a novel heterogeneous graph model MULAN, including two major
components, i.e., a type-aware encoder and a dimension-aware encoder.
Specifically, the type-aware encoder compensates for the loss of node type
information and better leverages graph heterogeneity in learning node
representations. Built upon transformer architecture, the dimension-aware
encoder is capable of capturing the latent interactions among the diverse node
features. With these components, the information of graph heterogeneity, node
features and graph structure can be comprehensively encoded in node
representations. We conduct extensive experiments on six heterogeneous
benchmark datasets, which demonstrates the superiority of MULAN over other
state-of-the-art competitors and also shows that MULAN is efficient.