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 this https URL

Seq-HGNN 是一种新颖的异构图神经网络，使用序列节点表示学习机制来表示每个节点的元路径表示序列， 并使用异构表示融合模块来聚合这些表示。 实验证明，Seq-HGNN 在信息检索方面的性能超过了现有的同类模型。