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.

翻译：近期，异构图神经网络（HGNN）在通过捕获节点层级的长程依赖性与异质性进行表示学习方面取得了显著成功。然而，现有研究鲜少深入探讨异构信息网络（HIN）中节点属性的利用问题。本文以节点分类这一基准任务为切入点，研究节点间属性差异对HGNN性能的影响，并通过实验发现：当分类对象的属性与其邻居节点存在显著差异时，典型模型会出现明显的性能衰减。为解决该问题，我们提出了一种创新的基于属性引导的异构信息网络Transformer表示学习模型（AGHINT），该模型能够在属性引导下更有效地聚合邻居节点信息。具体而言，AGHINT通过将高阶相似邻居特征直接融入学习过程，突破了原始图结构的约束，并基于节点属性差异调整了节点间的消息传递机制。在三个包含目标节点属性的真实异构图基准数据集上的大量实验结果表明，AGHINT的性能优于当前最先进方法。