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.

我们研究了异构信息网络中节点属性的差异对于异构图神经网络的性能影响，提出了一种新颖的以属性为指导的Transformer模型AGHINT，通过直接整合高阶相似的邻居特征并修改节点之间的信息传递机制来更有效地聚合邻居节点信息，实验结果表明AGHINT在节点分类任务上胜过了现有的最先进模型。

AGHINT：基于Transformer的异构信息网络上的属性引导表示学习