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Heterogeneous Graph Attention Network

arXiv 1903.07293 ↗First posted 18 Mar 2019cs.SIcited by 732

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Abstract (arXiv)

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Key facts. Cited by 732 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 138; 2022: 148; 2023: 118; 2024: 132; 2025: 118; 2026: 78.

732 papers.

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  1. 2021How Attentive are Graph Attention Networks?cited by 818
  2. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  3. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
  4. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  5. 2021AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networkscited by 173
  6. 2024GraphRouter: A Graph-based Router for LLM Selectionscited by 154
  7. 2021Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generationcited by 137
  8. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  9. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
  10. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124
  11. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  12. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
38 more
  1. 2022HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encodingcited by 109
  2. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  3. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  4. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  5. 2021Graph Representation Learning in Biomedicinecited by 79
  6. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  7. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  8. 2022A Survey on Graph Representation Learning Methodscited by 66
  9. 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
  10. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  11. 2021Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNscited by 51
  12. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  13. 2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
  14. 2023Patton: Language Model Pretraining on Text-Rich Networkscited by 49
  15. 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
  16. 2021Search to aggregate neighborhood for graph neural networkcited by 48
  17. 2022SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learningcited by 47
  18. 2022Heterogeneous Graph Masked Autoencoderscited by 47
  19. 2023Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learningcited by 47
  20. 2023HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learningcited by 45
  21. 2025RelGNN: Composite Message Passing for Relational Deep Learningcited by 45
  22. 2021Dynamic Network Embedding Surveycited by 44
  23. 2024HiGPT: Heterogeneous Graph Language Modelcited by 44
  24. 2023TacticAI: an AI assistant for football tacticscited by 43
  25. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  26. 2021Graph Meta Network for Multi-Behavior Recommendationcited by 38
  27. 2023Disentangled Representation Learning with Large Language Models for Text-Attributed Graphscited by 36
  28. 2023HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformercited by 35
  29. 2021Graph Communal Contrastive Learningcited by 34
  30. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  31. 2023Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applicationscited by 33
  32. 2021Network of Tensor Time Seriescited by 32
  33. 2022Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learningcited by 32
  34. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  35. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  36. 2023HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networkscited by 32
  37. 2021Heterogeneous Graph Representation Learning with Relation Awarenesscited by 31
  38. 2022Self-supervised Heterogeneous Graph Pre-training Based on Structural Clusteringcited by 30

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