Heterogeneous Graph Attention Network
Open accessAbstract
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 meta-path 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.
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Detecting edge anomalies requires sensitivity to both relational semantics and temporal evolution; HGNN-based models address this by learning edge-aware representations, capturing interaction dynamics across time, and incorporating context through attention-based aggregation mechanisms.
“Detecting these anomalies requires sensitivity to both relational semantics and temporal evolution. HGNN-based models address this by learning edge-aware representations, capturing interaction dynamics across time, and incorporating context through attention-based aggregation mechanisms. [18], [33]”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [18]
Traditional homogeneous graphs assume a single type of node and relation, which limits their expressiveness in real-world scenarios.
“Traditional homogeneous graphs assume a single type of node and relation, which limits their expressiveness in real-world scenarios.”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [18]
Heterogeneous graphs support multiple node and edge types, capturing rich semantic information essential for modeling diverse cybersecurity behaviors.
“In contrast, heterogeneous graphs [18], [19] involve multiple types of nodes and/or edges.”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [18]
Several obstacles remain before heterogeneous graph neural network methods for anomaly detection can be reliably deployed in real-world cybersecurity systems.
“Despite notable progress in heterogeneous graph neural networks (HGNNs) for anomaly detection, several obstacles remain before such methods can be reliably deployed in real-world cybersecurity systems [3], [18], [75].”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [18]
Generating representations of atoms, distances, and angles propagated along a molecular graph has allowed identification of the angle and direction of interactions between atoms.
“For example, generating representations of the atoms, distances, and angles to be propagated along the molecular graph has allowed us to identify the angle and direction of interactions between atoms [84].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [84]
Edges in molecular graphs can be added to indicate pairwise spatial distance between atoms, or directed with bond angles and rotations incorporated.
“Edges can also be added to indicate pairwise spatial distance between two atoms [72], or directed with information on bond angles and rotations incorporated into the molecular graph [84].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [84]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639 · see citing works
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 6,912 · see citing works
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416 · see citing works
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
Silva, G., Rodrigues, M., Teixeira, A., et al. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.sea.2024.20 · Cited by 5,266 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · see citing works
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845 · see citing works
Self-citationDong, Y., Chawla, N. V., & Swami, A. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098036 · Cited by 2,224 · see citing works
A new model for learning in graph domains
Gori, M., Monfardini, G., & Scarselli, F. (2006) · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · doi:10.1109/ijcnn.2005.1555942 · Cited by 2,075 · see citing works
Sun, Y., Han, J., Yan, X., Yu, P. S., & Wu, T. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/3402707.3402736 · Cited by 1,875 · see citing works
Self-citationAsymmetric Transitivity Preserving Graph Embedding
Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939751 · Cited by 1,298 · see citing works
Self-citationCui, P., Wang, X., Pei, J., & Zhu, W. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849727 · Cited by 1,274 · see citing works
Self-citationHeterogeneous Information Network Embedding for Recommendation
Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2833443 · Cited by 1,196 · see citing works
Self-citationA survey of heterogeneous information network analysis
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2598561 · Cited by 1,122 · see citing works
Self-citationCommunity Preserving Network Embedding
Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., & Yang, S. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10488 · Cited by 935 · see citing works
Self-citationLeveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
Hu, B., Shi, C., Zhao, W. X., & Yu, P. S. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219965 · Cited by 706 · see citing works
Self-citationMining heterogeneous information networks
Sun, Y., & Han, J. (2013) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2481244.2481248 · Cited by 619 · see citing works
Fu, T.-Y., Lee, W.-C., & Lei, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132953 · Cited by 602 · see citing works
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
Chen, T., & Sun, Y. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018735 · Cited by 212 · see citing works
Chen, H., Yin, H., Wang, W., Wang, H., Nguyen, Q. V. H., & Li, X. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219986 · Cited by 211 · see citing works
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information NetworksPreprint
Shang, J., Qu, M., Liu, J., Kaplan, L., Han, J., & Peng, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09769 · Cited by 142 · see citing works
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Shi, Y., Zhu, Q., Guo, F., Zhang, C., & Han, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220006 · Cited by 126 · see citing works
Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks
Han, X., Shi, C., Wang, S., et al. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/471 · Cited by 90 · see citing works
Self-citationSemi-supervised Clustering in Attributed Heterogeneous Information Networks
Li, X., Wu, Y., Ester, M., et al. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052576 · Cited by 86 · see citing works
Fan, Y., Hou, S., Zhang, Y., Ye, Y., & Abdulhayoglu, M. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219862 · Cited by 83 · see citing works
Self-citationJoint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
Sun, L., He, L., Huang, Z., et al. (2018) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/icbk.2018.00025 · Cited by 34 · see citing works
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works
Self-citationGraph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works
Heterogeneous Graph Transformer
Hu, Z., Dong, Y., Wang, K., & Sun, Y. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380027 · Cited by 1,646 · see citing works
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks
Jung, J.-W., Heo, H.-S., Tak, H., Shim, H.-J., Chung, J. S., Lee, B.-J., Yu, H.-J., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747766 · Cited by 399 · see citing works
Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
“72,83,84 aggregation transformations were designed to fuse diverse types of relations and attributes.”
A foundation model for clinician-centered drug repurposing
Huang, K., Chandak, P., Wang, Q., Havaldar, S., Vaid, A., Leskovec, J., Nadkarni, G. N., Glicksberg, B. S., Gehlenborg, N., & Zitnik, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03233-x · Cited by 249 · see citing works
“…, diffusion state distance (DSD) 17 , state-of-the-art GNN methods, including relational graph convolutional networks (RGCNs) 18,26 , heterogeneous graph transformer (HGT) 27 and heterogeneous attention networks (HANs) 28 and a natural language-processing model, BioBERT 29 (Supplementary Note 4).”
BackgroundExtending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
Wang, Q., Huang, K., Chandak, P., Zitnik, M., & Gehlenborg, N. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209435 · Cited by 82 · see citing works
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
Mao, C., Yao, L., & Luo, Y. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104000 · Cited by 70 · see citing works
Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17575 · Cited by 63 · see citing works
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3592788 · Cited by 34 · see citing works
PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph
Yu, D., Hu, Y., Li, Y., & Zhao, L. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671738 · Cited by 15 · see citing works
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
Jiang, L., Ryan, R., Li, Q., & Ferdosian, N. (2025) · arXiv · doi:10.48550/arxiv.2510.26307
“[18], [33] 3) Subgraph Anomalies: Subgraph anomalies arise when a group of nodes and edges collectively exhibit behavior that deviates from the structural or semantic norms of the overall graph.”
Influential
- Authors
- Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- West Virginia University (US)
- Tsinghua University (CN)
- University of Illinois Chicago (US)
- Venue
- The World Wide Web Conference
- Publisher
- ACM
- Published
- 2019-05-13
- Pages
- 2022-2032
- OpenAlex
- W2911286998
- Cited by
- 3,191
- References
- 35
- Citations by year
- 2026: 392 · 2025: 618 · 2024: 561 · 2023: 561 · 2022: 444 · 2021: 408 · 2020: 184 · 2019: 21 · 2018: 1
- FWCI
- 101.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Tencent
- National Natural Science Foundation of China (61702296, 61532006, 61772082)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- heterogeneous graph neural networks, graph attention networks, hierarchical attention, meta-paths, node embeddings, feature aggregation, interpretability
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