Attention-based graph neural networks: a survey

• Abu-El-Haija S, Perozzi B, Al-Rfou R et al (2018) Watch your step: learning node embeddings via graph attention. Adv Neural Inf Processing Syst. https://doi.org/10.48550/arXiv.1710.09599

  Article  Google Scholar 

• Ahmad WU, Peng N, Chang KW (2021) Gate: graph attention transformer encoder for cross-lingual relation and event extraction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 12,462–12,470

• Alon U, Yahav E (2020) On the bottleneck of graph neural networks and its practical implications. Mach Learn 89:5–35

  Google Scholar 

• Baek J, Kang M, Hwang SJ (2021) Accurate learning of graph representations with graph multiset pooling. In: The Ninth International Conference on Learning Representations, The International Conference on Learning Representations (ICLR)

• Bai S, Zhang F, Torr PH (2021) Hypergraph convolution and hypergraph attention. Pattern Recognit 110(107):637

  Google Scholar 

• Battaglia PW, Hamrick JB, Bapst V, et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261

• Battiston F, Amico E, Barrat A et al (2021) The physics of higher-order interactions in complex systems. Nat Phys 17(10):1093–1098

  Google Scholar 

• Bo D, Wang X, Shi C, et al (2021) Beyond low-frequency information in graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 3950–3957

• Brauwers G, Frasincar F (2021) A general survey on attention mechanisms in deep learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2021.3126456

  Article  Google Scholar 

• Brody S, Alon U, Yahav E (2021) How attentive are graph attention networks? In: International Conference on Learning Representations

• Bruna J, Zaremba W, Szlam A, et al (2014) Spectral networks and locally connected networks on graphs. In: International Conference on Learning Representations (ICLR2014), CBLS, April 2014, pp http–openreview

• Busbridge D, Sherburn D, Cavallo P, et al (2019) Relational graph attention networks. arXiv preprint arXiv:1904.05811

• Cai D, Lam W (2020) Graph transformer for graph-to-sequence learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7464–7471

• Cao M, Ma X, Zhu K, et al (2020) Heterogeneous information network embedding with convolutional graph attention networks. In: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–8

• Cen Y, Zou X, Zhang J, et al (2019) Representation learning for attributed multiplex heterogeneous network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1358–1368

• Chami I, Ying Z, Ré C, et al (2019) Hyperbolic graph convolutional neural networks. Advances in neural information processing systems 32

• Chaudhari S, Mithal V, Polatkan G et al (2021) An attentive survey of attention models. ACM Trans Intell Syst Technol (TIST) 12(5):1–32

  Google Scholar 

• Chen B, Barzilay R, Jaakkola T (2019) Path-augmented graph transformer network. Mach Learn. https://doi.org/10.48550/arXiv.1905.12712

  Article  Google Scholar 

• Cheng R, Li Q (2021) Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 55–62

• Choi E, Bahadori MT, Song L, et al (2017) Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 787–795

• Choi J (2022) Personalized pagerank graph attention networks. ICASSP 2022–2022 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), IEEE, pp 3578–3582

• Cini A, Marisca I, Bianchi FM, et al (2022) Scalable spatiotemporal graph neural networks. arXiv preprint arXiv:2209.06520

• Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Processing Syst. https://doi.org/10.48550/arXiv.1606.09375

  Article  Google Scholar 

• Dwivedi VP, Bresson X (2020) A generalization of transformer networks to graphs. Mach Learn. https://doi.org/10.48550/arXiv.2012.09699

  Article  Google Scholar 

• Ektefaie Y, Dasoulas G, Noori A et al (2023) Multimodal learning with graphs. Nat Mach Intell. https://doi.org/10.1038/s42256-023-00624-6

  Article  Google Scholar 

• Fang X, Huang J, Wang F, et al (2020) Constgat: Contextual spatial-temporal graph attention network for travel time estimation at baidu maps. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2697–2705

• Fathy A, Li K (2020) Temporalgat: attention-based dynamic graph representation learning. Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 413–423

  Google Scholar 

• Feng B, Wang Y, Ding Y (2021) Uag: Uncertainty-aware attention graph neural network for defending adversarial attacks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7404–7412

• Gao C, Wang X, He X, et al (2022) Graph neural networks for recommender system. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp 1623–1625

• Gao H, Ji S (2019) Graph representation learning via hard and channel-wise attention networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 741–749

• Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: State of the art and challenges. IEEE Access 9:22

  Google Scholar 

• Gilmer J, Schoenholz SS, Riley PF, et al (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, PMLR, pp 1263–1272

• Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 855–864

• Gulcehre C, Denil M, Malinowski M et al (2018) Hyperbolic attention networks. Neural Evol Comput. https://doi.org/10.48550/arXiv.1805.09786

  Article  Google Scholar 

• Guo MH, Xu TX, Liu JJ et al (2022) Attention mechanisms in computer vision: a survey. Comput Vis Media 3:1–38

  Google Scholar 

• Guo S, Lin Y, Feng N, et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 922–929

• Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Processing Syst. https://doi.org/10.1093/bioinformatics/btad135

  Article  Google Scholar 

• He S, Shin KG (2020) Towards fine-grained flow forecasting: a graph attention approach for bike sharing systems. Proc Web Conf 2020:88–98

  Google Scholar 

• He T, Ong YS, Bai L (2021) Learning conjoint attentions for graph neural nets. Adv Neural Inf Proc Syst 34:2641–2653

  Google Scholar 

• Hong H, Guo H, Lin Y, et al (2020) An attention-based graph neural network for heterogeneous structural learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4132–4139

• Hu W, Fey M, Zitnik M et al (2020) Open graph benchmark: datasets for machine learning on graphs. Adv Neural Inf Processing Syst 33:22

  Google Scholar 

• Hu Z, Dong Y, Wang K et al (2020) Heterogeneous graph transformer. Proc Web Conf 2020:2704–2710

  Google Scholar 

• Huang B, Carley KM (2019) Syntax-aware aspect level sentiment classification with graph attention networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 5469–5477

• Huang J, Li Z, Li N et al (2019a) Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 6480–6489

• Huang J, Shen H, Hou L et al (2019) Signed graph attention networks. Int Conf Artif Neural Netw. Springer, Berlin, pp 566–577

  Google Scholar 

• Jiang W, Luo J (2021) Graph neural network for traffic forecasting: A survey. arXiv preprint arXiv:2101.11174

• Jung J, Jung J, Kang U (2021a) Learning to walk across time for interpretable temporal knowledge graph completion. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 786–795

• Jw Jung, Heo HS, Yu HJ et al (2021) Graph attention networks for speaker verification. ICASSP 2021–2021 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), IEEE, pp 6149–6153

• Kazemi SM, Goel R, Jain K et al (2020) Representation learning for dynamic graphs: a survey. J Mach Learn Res 21(70):1–73

  MathSciNet  MATH  Google Scholar 

• Kim BH, Ye JC, Kim JJ (2021) Learning dynamic graph representation of brain connectome with Spatio-temporal attention. Adv Neural Inf Proc Syst 34:4314–4327

  Google Scholar 

• Kim D, Oh AH (2021) How to find your friendly neighborhood: Graph attention design with self-supervision. In: The Ninth International Conference on Learning Representations (ICLR 2021), International Conference on Learning Representations (ICLR 2021)

• Kim J, Oh S, Hong S (2021) Transformers generalize deepsets and can be extended to graphs & hypergraphs. Adv Neural Inf Proc Syst 34:28,016-28,028

  Google Scholar 

• Kim J, Yoon S, Kim D et al (2021c) Structured co-reference graph attention for video-grounded dialogue. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 1789–1797

• Klicpera J, Bojchevski A, Günnemann S (2018) Predict then propagate: graph neural networks meet personalized Pagerank. Comput Sci. https://doi.org/10.48550/arXiv.1810.05997

  Article  Google Scholar 

• Klicpera J, Weißenberger S, Günnemann S (2019) Diffusion improves graph learning. arXiv preprint arXiv:1911.05485

• Knyazev B, Taylor GW, Amer M (2019) Understanding attention and generalization in graph neural networks. Adv Neural Inf Proc Syst. https://doi.org/10.48550/arXiv.1905.02850

  Article  Google Scholar 

• Koncel-Kedziorski R, Bekal D, Luan Y, et al (2019) Text generation from knowledge graphs with graph transformers. In: 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp 2284–2293

• Kreuzer D, Beaini D, Hamilton W et al (2021) Rethinking graph transformers with spectral attention. Adv Neural Inf Proc Syst 34:21,618-21,629

  Google Scholar 

• Lee J, Lee I, Kang J (2019) Self-attention graph pooling. International conference on machine learning. PMLR, New York, pp 3734–3743

  Google Scholar 

• Lee JB, Rossi R, Kong X (2018) Graph classification using structural attention. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1666–1674

• Lee JB, Rossi RA, Kim S et al (2019) Attention models in graphs: a survey. ACM Trans Knowl Dis Data (TKDD) 13(6):1–25

  Google Scholar 

• Li J, Liu X, Zong Z et al (2020a) Graph attention based proposal 3d convnets for action detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4626–4633

• Li L, Gan Z, Cheng Y et al (2019) Relation-aware graph attention network for visual question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10,313–10,322

• Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI press, pp 3538–3545

• Li X, Shang Y, Cao Y et al (2020b) Type-aware anchor link prediction across heterogeneous networks based on graph attention network. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 147–155

• Li Y, Zemel R, Brockschmidt M et al (2016) Gated graph sequence neural networks. In: Proceedings of ICLR’16

• Li Y, Tian Y, Zhang J et al (2020c) Learning signed network embedding via graph attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4772–4779

• Liang Y, Ke S, Zhang J et al (2018) Geoman: Multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp 3428–3434

• Liao R, Li Y, Song Y et al (2019) Efficient graph generation with graph recurrent attention networks. Adv Neural Inf Proc Syst. https://doi.org/10.1016/j.aiopen.2021.01.001

  Article  Google Scholar 

• Lin L, Wang H (2020) Graph attention networks over edge content-based channels. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1819–1827

• Lin L, Blaser E, Wang H (2022) Graph embedding with hierarchical attentive membership. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp 582–590

• Lin T, Wang Y, Liu X et al (2021) A survey of transformers. arXiv preprint arXiv:2106.04554

• Liu M, Gao H, Ji S (2020) Towards deeper graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 338–348

• Liu M, Wang Z, Ji S (2021) Non-local graph neural networks. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.4108/eetel.v8i3.3461

  Article  Google Scholar 

• Liu S, Chen Z, Liu H et al (2019a) User-video co-attention network for personalized micro-video recommendation. In: The World Wide Web Conference, pp 3020–3026

• Liu Z, Chen C, Li L et al (2019b) Geniepath: Graph neural networks with adaptive receptive paths. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4424–4431

• Long Y, Wu M, Liu Y et al (2021) Graph contextualized attention network for predicting synthetic lethality in human cancers. Bioinformatics 37(16):2432–2440

  Google Scholar 

• Lu Y, Wang X, Shi C et al (2019) Temporal network embedding with micro-and macro-dynamics. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp 469–478

• Luan S, Hua C, Lu Q et al (2021) Is heterophily a real nightmare for graph neural networks to do node classification? arXiv preprint arXiv:2109.05641

• Lv Q, Ding M, Liu Q et al (2021) Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 1150–1160

• Ma N, Mazumder S, Wang H et al (2020) Entity-aware dependency-based deep graph attention network for comparative preference classification. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2020)

• Maron H, Ben-Hamu H, Serviansky H et al (2019) Provably powerful graph networks. Adv Neural Inf Proc Syst. https://doi.org/10.1038/s43246-022-00315-6

  Article  Google Scholar 

• Mei G, Pan L, Liu S (2022) Heterogeneous graph embedding by aggregating meta-path and meta-structure through attention mechanism. Neurocomputing 468:276–285

  Google Scholar 

• Min E, Chen R, Bian Y et al (2022) Transformer for graphs: An overview from architecture perspective. arXiv preprint arXiv:2202.08455

• Mou C, Zhang J, Wu Z (2021) Dynamic attentive graph learning for image restoration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4328–4337

• Nathani D, Chauhan J, Sharma C et al (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 4710–4723

• Nguyen DQ, Nguyen TD, Phung D (2019) Universal graph transformer self-attention networks. arXiv preprint arXiv:1909.11855

• Peng H, Li J, Gong Q et al (2020) Motif-matching based subgraph-level attentional convolutional network for graph classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 5387–5394

• Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 701–710

• Phuong M, Hutter M (2022) Formal algorithms for transformers. arXiv preprint arXiv:2207.09238

• Qin L, Li Z, Che W et al (2021a) Co-gat: A co-interactive graph attention network for joint dialog act recognition and sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 13,709–13,717

• Qin X, Sheikh N, Reinwald B et al (2021b) Relation-aware graph attention model with adaptive self-adversarial training. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 9368–9376

• Qu M, Tang J, Shang J et al (2017) An attention-based collaboration framework for multi-view network representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1767–1776

• Rong Y, Huang W, Xu T et al (2019) Dropedge: Towards deep graph convolutional networks on node classification. In: International Conference on Learning Representations

• Rong Y, Bian Y, Xu T et al (2020) Self-supervised graph transformer on large-scale molecular data. Adv Neural Inf Proc Syst 33:12,559-12,571

  Google Scholar 

• Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

  Google Scholar 

• Ruiz L, Gama F, Ribeiro A (2020) Gated graph recurrent neural networks. IEEE Tran Signal Proc 68:6303–6318

  MathSciNet  MATH  Google Scholar 

• Sankar A, Wu Y, Gou L et al (2018) Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430

• Scarselli F, Gori M, Tsoi AC et al (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

  Google Scholar 

• Schuetz MJ, Brubaker JK, Katzgraber HG (2022) Combinatorial optimization with physics-inspired graph neural networks. Nat Mach Intell 4(4):367–377

  Google Scholar 

• Seo SW, Song YY, Yang JY et al (2021) Gta: Graph truncated attention for retrosynthesis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 531–539

• Shang C, Liu Q, Chen KS et al (2018) Edge attention-based multi-relational graph convolutional networks. arXiv preprint arXiv:1802.04944

• Shi M, Huang Y, Zhu X et al (2021a) Gaen: Graph attention evolving networks. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp 1541–1547

• Shi Y, Huang Z, Feng S et al (2021b) Masked label prediction: Unified message passing model for semi-supervised classification. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, pp 1548–1554

• Shukla SN, Marlin BM (2021) Multi-time attention networks for irregularly sampled time series. arXiv preprint arXiv:2101.10318

• Silva VF, Silva ME, Ribeiro P et al (2021) Time series analysis via network science: concepts and algorithms. Wiley Interdiscip Rev 11(3):e1404

  Google Scholar 

• Stachenfeld K, Godwin J, Battaglia P (2020) Graph networks with spectral message passing. arXiv preprint arXiv:2101.00079

• Stärk H, Beaini D, Corso G et al (2021) 3d infomax improves gnns for molecular property prediction. arXiv preprint arXiv:2110.04126

• Su X, Xue S, Liu F et al (2022) A comprehensive survey on community detection with deep learning. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3137396

  Article  Google Scholar 

• Sun Q, Liu H, He J et al (2020) Dagc: Employing dual attention and graph convolution for point cloud based place recognition. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp 224–232

• Suresh S, Budde V, Neville J et al (2021) Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 1541–1551

• Tang H, Liang X, Wu B et al (2021) Graph ensemble networks for semi-supervised embedding learning. International Conference on Knowledge Science. Springer, Engineering and Management, pp 408–420

• Tang J, Qu M, Wang M et al (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp 1067–1077

• Tao Z, Wei Y, Wang X et al (2020) Mgat: multimodal graph attention network for recommendation. Inf Proc Manag 57(5):102,277

  Google Scholar 

• Tay Y, Dehghani M, Bahri D et al (2020) Efficient transformers: a survey. ACM Compu Surveys (CSUR). https://doi.org/10.48550/arXiv.2009.06732

  Article  Google Scholar 

• Thekumparampil KK, Wang C, Oh S et al (2018) Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735

• Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Adv Neural Inf Proc Syst 30:5998–6008

  Google Scholar 

• Veličković P, Cucurull G, Casanova A et al (2018) Graph attention networks. In: International Conference on Learning Representations

• Vijaikumar M, Hada D, Shevade S (2021) Hypertenet: Hypergraph and transformer-based neural network for personalized list continuation. In: 2021 IEEE International Conference on Data Mining (ICDM), IEEE, pp 1210–1215

• Wang G, Ying R, Huang J et al (2019a) Improving graph attention networks with large margin-based constraints. arXiv preprint arXiv:1910.11945

• Wang G, Ying R, Huang J et al (2021) Multi-hop attention graph neural networks. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)

• Wang K, Shen W, Yang Y et al (2020a) Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 3229–3238

• Wang P, Han J, Li C et al (2019b) Logic attention based neighborhood aggregation for inductive knowledge graph embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7152–7159

• Wang X, He X, Cao Y et al (2019c) Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 950–958

• Wang X, Ji H, Shi C et al (2019d) Heterogeneous graph attention network. In: The World Wide Web Conference, pp 2022–2032

• Wang X, Zhu M, Bo D et al (2020b) Am-gcn: Adaptive multi-channel graph convolutional networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1243–1253

• Wang X, Bo D, Shi C et al (2022) A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Trans Big Data 9(2):415–436

  Google Scholar 

• Wang Y, Derr T (2021) Tree decomposed graph neural network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 2040–2049

• Wang Z, Lei Y, Li W (2020) Neighborhood attention networks with adversarial learning for link prediction. IEEE Trans Neural Netw Learn Syst 32(8):3653–3663

  Google Scholar 

• Welling M, Kipf TN (2016) Semi-supervised classification with graph convolutional networks. In: J. International Conference on Learning Representations (ICLR 2017)

• Wen Q, Zhou T, Zhang C et al (2022) Transformers in time series: A survey. arXiv preprint arXiv:2202.07125

• Wu F, Souza A, Zhang T et al (2019a) Simplifying graph convolutional networks. In: International Conference on Machine Learning, PMLR, pp 6861–6871

• Wu J, Shi W, Cao X et al (2021a) Disenkgat: knowledge graph embedding with disentangled graph attention network. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 2140–2149

• Wu L, Chen Y, Shen K et al (2021b) Graph neural networks for natural language processing: A survey. arXiv preprint arXiv:2106.06090

• Wu Q, Zhang H, Gao X et al (2019b) Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The World Wide Web Conference, pp 2091–2102

• Wu S, Sun F, Zhang W et al (2020) Graph neural networks in recommender systems: a survey. ACM Comput Surv (CSUR). https://doi.org/10.7717/peerj-cs.1166

  Article  Google Scholar 

• Wu Z, Pan S, Chen F et al (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24

  MathSciNet  Google Scholar 

• Xia F, Sun K, Yu S et al (2021) Graph learning: a survey. IEEE Trans Artif Intell 2(2):109–127

  Google Scholar 

• Xia L, Huang C, Xu Y et al (2021b) Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 4486–4493

• Xie Y, Zhang Y, Gong M et al (2020) Mgat: multi-view graph attention networks. Neural Netw 132:180–189

  MATH  Google Scholar 

• Xu D, Ruan C, Korpeoglu E et al (2020) Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962

• Xu K, Hu W, Leskovec J et al (2018a) How powerful are graph neural networks? In: International Conference on Learning Representations

• Xu K, Li C, Tian Y et al (2018b) Representation learning on graphs with jumping knowledge networks. In: International conference on machine learning, PMLR, pp 5453–5462

• Xu X, Zu S, Gao C et al (2018c) Modeling attention flow on graphs. arXiv preprint arXiv:1811.00497

• Xu Y, Wang L, Wang Y et al (2022) Adaptive trajectory prediction via transferable gnn. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6520–6531

• Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI Conference on Artificial Intelligence

• Yang J, Liu Z, Xiao S et al (2021) Graphformers: Gnn-nested transformers for representation learning on textual graph. Adv Neural Inf Processing Syst 34:28,798-28,810

  Google Scholar 

• Yang L, Wu F, Gu J et al (2020) Graph attention topic modeling network. Proc Web Conf 2020:144–154

  Google Scholar 

• Yang L, Li M, Liu L et al (2021) Diverse message passing for attribute with heterophily. Adv Neural Inf Processing Syst 34:4751–4763

  Google Scholar 

• Yang M, Zhou M, Li Z et al (2022a) Hyperbolic graph neural networks: A review of methods and applications. arXiv preprint arXiv:2202.13852

• Yang R, Shi J, Yang Y et al (2021) Effective and scalable clustering on massive attributed graphs. Proc Web Conf 2021:3675–3687

  Google Scholar 

• Yang S, Hu B, Zhang Z et al (2021) Inductive link prediction with interactive structure learning on attributed graph. Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, pp 383–398

  Google Scholar 

• Yang T, Hu L, Shi C et al (2021) Hgat: heterogeneous graph attention networks for semi-supervised short text classification. ACM Trans Inf Syst(TOIS) 39(3):1–29

  Google Scholar 

• Yang Y, Wang X, Song M et al (2019) Spagan: shortest path graph attention network. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp 4099–4105

• Yang Y, Qiu J, Song M et al (2020b) Distilling knowledge from graph convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 7074–7083

• Yang Y, Jiao L, Liu X et al (2022b) Transformers meet visual learning understanding: A comprehensive review. arXiv preprint arXiv:2203.12944

• Yang Z, Dong S (2020) Hagerec: hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation. Knowl-Based Syst 204(106):194

  Google Scholar 

• Ying C, Cai T, Luo S et al (2021) Do transformers really perform badly for graph representation? Adv Neural Inf Proc Syst 34:28,877-28,888

  Google Scholar 

• Ying Z, You J, Morris C et al (2018) Hierarchical graph representation learning with differentiable pooling. Adv Neural Inf Processing Systms. https://doi.org/10.48550/arXiv.1806.08804

  Article  Google Scholar 

• Yuan H, Yu H, Gui S et al (2020) Explainability in graph neural networks: A taxonomic survey. arXiv preprint arXiv:2012.15445

• Yuan H, Yu H, Wang J et al (2021a) On explainability of graph neural networks via subgraph explorations. In: International Conference on Machine Learning, PMLR, pp 12,241–12,252

• Yuan J, Yu H, Cao M et al (2021b) Semi-supervised and self-supervised classification with multi-view graph neural networks. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 2466–2476

• Yun S, Jeong M, Kim R et al (2019) Graph transformer networks. Adv Neural Inf Processing Syst. https://doi.org/10.1016/j.neunet.2022.05.026

  Article  Google Scholar 

• Zeng H, Zhou H, Srivastava A et al (2019) Graphsaint: Graph sampling based inductive learning method. In: International Conference on Learning Representations

• Zhang C, Gao J (2021) Hype-han: Hyperbolic hierarchical attention network for semantic embedding. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp 3990–3996

• Zhang J, Shi X, Xie J et al (2018) Gaan: Gated attention networks for learning on large and spatiotemporal graphs. In: 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018

• Zhang J, Zhang H, Xia C et al (2020a) Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140

• Zhang J, Gao M, Yu J et al (2021a) Double-scale self-supervised hypergraph learning for group recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 2557–2567

• Zhang J, Chen Y, Xiao X et al (2022) Learnable hypergraph laplacian for hypergraph learning. ICASSP 2022–2022 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), IEEE, pp 4503–4507

• Zhang J, Li F, Xiao X et al (2022b) Hypergraph convolutional networks via equivalency between hypergraphs and undirected graphs. arXiv preprint arXiv:2203.16939

• Zhang K, Zhu Y, Wang J et al (2019) Adaptive structural fingerprints for graph attention networks. In: International Conference on Learning Representations

• Zhang R, Zou Y, Ma J (2020b) Hyper-sagnn: a self-attention based graph neural network for hypergraphs. In: International Conference on Learning Representations (ICLR)

• Zhang S, Xie L (2020) Improving attention mechanism in graph neural networks via cardinality preservation. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), NIH Public Access, p 1395

• Zhang W, Chen Z, Dong C et al (2021b) Graph-based tri-attention network for answer ranking in cqa. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 14,463–14,471

• Zhang W, Yin Z, Sheng Z et al (2022c) Graph attention multi-layer perceptron. arXiv preprint arXiv:2206.04355

• Zhang X, Zeman M, Tsiligkaridis T et al (2021c) Graph-guided network for irregularly sampled multivariate time series. In: International Conference on Learning Representations (ICLR)

• Zhang Y, Wang X, Shi C et al (2021) Hyperbolic graph attention network. IEEE Trans Big Data 8(6):1690–1701

  Google Scholar 

• Zhang Z, Zhuang F, Zhu H et al (2020c) Relational graph neural network with hierarchical attention for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 9612–9619

• Zhao H, Wang Y, Duan J et al (2020) Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM), IEEE, pp 841–850

• Zhao Z, Gao B, Zheng VW et al (2017) Link prediction via ranking metric dual-level attention network learning. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp 3525–3531

• Zheng C, Fan X, Wang C et al (2020) Gman: A graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 1234–1241

• Zheng X, Liu Y, Pan S et al (2022) Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082

• Zhou J, Cui G, Hu S et al (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81

  Google Scholar 

• Zhou Y, Zheng H, Huang X et al (2022) Graph neural networks: taxonomy, advances, and trends. ACM Trans Intell Syst Technol (TIST) 13(1):1–54

  Google Scholar 

• Zhu Y, Lyu F, Hu C et al (2022) Learnable encoder-decoder architecture for dynamic graph: A survey. arXiv preprint arXiv:2203.10480

• Zuo Y, Liu G, Lin H et al (2018) Embedding temporal network via neighborhood formation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2857–2866

Download references