mlsanityData to 14 Oct 2026

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

arXiv 2305.10771 ↗First posted 18 May 2023cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

23 of 35 references.

References · 23

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11 more
  1. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  2. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  3. 2022Graph Attention Multi-Layer Perceptroncited by 83
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  5. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  6. 2020Interpretable and Efficient Heterogeneous Graph Convolutional Networkcited by 48
  7. 2020Scalable Graph Neural Networks for Heterogeneous Graphscited by 31
  8. 2021Heterogeneous Graph Representation Learning with Relation Awarenesscited by 31
  9. 2021Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendationcited by 22
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  11. 2021HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modelingcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneitycited by 3
  2. 2024LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphscited by 0
  3. 2026HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophilycited by 0