AGHINT: Attribute-Guided Representation Learning on Heterogeneous
  Information Networks with Transformer
Jinhui Yuan Shan Lu Peibo Duan Jieyue He
Recently, heterogeneous graph neural networks (HGNNs) have achieved
impressive success in representation learning by capturing long-range
dependencies and heterogeneity at the node level. However, few existing studies
have delved into the utilization of node attributes in heterogeneous
information networks (HINs). In this paper, we investigate the impact of
inter-node attribute disparities on HGNNs performance within the benchmark
task, i.e., node classification, and empirically find that typical models
exhibit significant performance decline when classifying nodes whose attributes
markedly differ from their neighbors. To alleviate this issue, we propose a
novel Attribute-Guided heterogeneous Information Networks representation
learning model with Transformer (AGHINT), which allows a more effective
aggregation of neighbor node information under the guidance of attributes.
Specifically, AGHINT transcends the constraints of the original graph structure
by directly integrating higher-order similar neighbor features into the
learning process and modifies the message-passing mechanism between nodes based
on their attribute disparities. Extensive experimental results on three
real-world heterogeneous graph benchmarks with target node attributes
demonstrate that AGHINT outperforms the state-of-the-art.