Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in heterogeneous graphs. However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing. %insufficient mining of information. To this end, we propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. To avoid the information loss caused by the single vector node representation, we first design a sequential node representation learning mechanism to represent each node as a sequence of meta-path representations during the node message passing. Then we propose a heterogeneous representation fusion module, empowering Seq-HGNN to identify important meta-paths and aggregate their representations into a compact one. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB). Experimental results show that our proposed method outperforms state-of-the-art baselines in both accuracy and efficiency. The source code is available at https://github.com/nobrowning/SEQ_HGNN.

翻译：近年来，异构图神经网络（HGNN）在信息检索（IR）应用中取得了快速发展。现有HGNN通过设计多种定制化的图卷积操作来捕获异构图中的结构与语义信息。然而，这些方法通常将每个节点表示为多层图卷积计算中的单一向量，导致高层图卷积层无法区分来自不同关系和不同阶次的信息，从而造成消息传递过程中的信息损失。为此，本文提出一种基于序列化节点表示的新型异构图神经网络——Seq-HGNN。为避免单一向量节点表示带来的信息损失，我们首先设计了一种序列化节点表示学习机制，在节点消息传递过程中将每个节点表示为元路径表示序列。随后提出异质表示融合模块，使Seq-HGNN能够识别重要元路径并将其表示聚合为紧凑形式。在异构图基准（HGB）和开放图基准（OGB）的四个广泛使用的数据集上开展的大量实验表明，该方法在准确性和效率上均优于现有最优基线方法。源代码见https://github.com/nobrowning/SEQ_HGNN。