{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:19:33Z","timestamp":1775758773119,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Guangdong Provincial Key Laboratory of Intellectual Property and Big Data","award":["2018B030322016"],"award-info":[{"award-number":["2018B030322016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the higher-order information and global feature similarity information in heterogeneous graphs. In this paper, we propose a Multi-View Heterogeneous graph neural network (MV-HGNN) to aggregate these information. Firstly, two auxiliary views, specifically a global feature similarity view and a graph diffusion view, are generated from the original heterogeneous graph. Secondly, MV-HGNN performs two message-passing strategies to get the representation of different views. Subsequently, a transformer-based aggregator is used to get the semantic information. Subsequently, the representations of the three views are fused into a final composite representation. We evaluate our method on the node classification task over three commonly used heterogeneous graph datasets, and the results demonstrate that our proposed MV-HGNN significantly outperforms state-of-the-art baselines.<\/jats:p>","DOI":"10.1007\/s41019-024-00253-y","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T11:04:47Z","timestamp":1719227087000},"page":"294-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multi-view Heterogeneous Graph Neural Networks for Node Classification"],"prefix":"10.1007","volume":"9","author":[{"given":"Xi","family":"Zeng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2059-8818","authenticated-orcid":false,"given":"Fang-Yuan","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qing-Yun","family":"Dai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"issue":"1","key":"253_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","volume":"29","author":"C Shi","year":"2016","unstructured":"Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17\u201337","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"253_CR2","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"issue":"1","key":"253_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"253_CR4","doi-asserted-by":"publisher","first-page":"10583","DOI":"10.1109\/TKDE.2023.3265598","volume":"35","author":"Y Dong","year":"2023","unstructured":"Dong Y, Ma J, Wang S, Chen C, Li J (2023) Fairness in graph mining: a survey. IEEE Trans Knowl Data Eng 35:10583\u201310602","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"253_CR5","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1109\/TETCI.2022.3222545","volume":"7","author":"M Nie","year":"2023","unstructured":"Nie M, Chen D, Wang D (2023) Reinforcement learning on graphs: A survey. IEEE Trans Emerg Topics Comput Intell 7(4):1065\u20131082. https:\/\/doi.org\/10.1109\/TETCI.2022.3222545","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"253_CR6","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"253_CR7","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den\u00a0Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference, Springer, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"253_CR8","unstructured":"Berg Rvd, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263"},{"key":"253_CR9","doi-asserted-by":"crossref","unstructured":"Zhang J, Shi X, Zhao S, King I (2019) Star-gcn: stacked and reconstructed graph convolutional networks for recommender systems. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 4264\u20134270","DOI":"10.24963\/ijcai.2019\/592"},{"issue":"4","key":"253_CR10","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/TETCI.2022.3225550","volume":"7","author":"F Cheng","year":"2023","unstructured":"Cheng F, Zhou C, Liu X, Wang Q, Qiu J, Zhang L (2023) Graph-based feature selection in classification: structure and node dynamic mechanisms. IEEE Trans Emerg Topics Comput Intell 7(4):1314\u20131328. https:\/\/doi.org\/10.1109\/TETCI.2022.3225550","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"issue":"3","key":"253_CR11","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1109\/TETCI.2022.3232821","volume":"7","author":"A Takiddin","year":"2023","unstructured":"Takiddin A, Atat R, Ismail M, Boyaci O, Davis KR, Serpedin E (2023) Generalized graph neural network-based detection of false data injection attacks in smart grids. IEEE Trans Emerg Topics Comput Intell 7(3):618\u2013630. https:\/\/doi.org\/10.1109\/TETCI.2022.3232821","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"253_CR12","first-page":"1","volume":"19","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Jia Y, Hou Y, Yu X (2024) Explicit behavior interaction with heterogeneous graph for multi-behavior recommendation. Data Sci Eng 19:1\u201319","journal-title":"Data Sci Eng"},{"key":"253_CR13","doi-asserted-by":"crossref","unstructured":"Yang C, Pal A, Zhai A, Pancha N, Han J, Rosenberg C, Leskovec J (2020) Multisage: empowering gcn with contextualized multi-embeddings on web-scale multipartite networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2434\u20132443","DOI":"10.1145\/3394486.3403293"},{"key":"253_CR14","doi-asserted-by":"crossref","unstructured":"Tian Y, Dong K, Zhang C, Zhang C, Chawla NV (2023) Heterogeneous graph masked autoencoders. In: Proceedings of the AAAI conference on artificial intelligence, vol. 37, pp 9997\u201310005","DOI":"10.1609\/aaai.v37i8.26192"},{"key":"253_CR15","doi-asserted-by":"crossref","unstructured":"Chen M, Huang C, Xia L, Wei W, Xu Y, Luo R (2023) Heterogeneous graph contrastive learning for recommendation. In: Proceedings of the 16th ACM international conference on web search and data mining, pp 544\u2013552","DOI":"10.1145\/3539597.3570484"},{"issue":"2","key":"253_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/TBDATA.2022.3177455","volume":"9","author":"X Wang","year":"2022","unstructured":"Wang X, Bo D, Shi C, Fan S, Ye Y, Philip SY (2022) A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Trans Big Data 9(2):415\u2013436","journal-title":"IEEE Trans Big Data"},{"key":"253_CR17","unstructured":"Klicpera J, Wei\u00dfenberger S, G\u00fcnnemann S (2019) Diffusion improves graph learning. arXiv preprint arXiv:1911.05485"},{"key":"253_CR18","unstructured":"Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: International Conference on machine learning, PMLR, pp 4116\u20134126"},{"key":"253_CR19","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y (2020) Graph contrastive learning with augmentations. Adv Neural Inf Process Syst 33:5812\u20135823","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"253_CR20","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s41019-022-00190-8","volume":"7","author":"M-S Chen","year":"2022","unstructured":"Chen M-S, Lin J-Q, Li X-L, Liu B-Y, Wang C-D, Huang D, Lai J-H (2022) Representation learning in multi-view clustering: a literature review. Data Sci Eng 7(3):225\u2013241","journal-title":"Data Sci Eng"},{"key":"253_CR21","first-page":"3844","volume":"29","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844\u20133852","journal-title":"Adv Neural Inf Process Syst"},{"key":"253_CR22","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"issue":"20","key":"253_CR23","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y et al (2017) Graph attention networks. stat 1050(20):10\u201348550","journal-title":"stat"},{"key":"253_CR24","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, pp 1025\u20131035"},{"key":"253_CR25","doi-asserted-by":"crossref","unstructured":"Tsitsulin A, Mottin D, Karras P, M\u00fcller E (2018) Verse: versatile graph embeddings from similarity measures. In: Proceedings of the 2018 world wide web conference, pp 539\u2013548","DOI":"10.1145\/3178876.3186120"},{"key":"253_CR26","doi-asserted-by":"crossref","unstructured":"Wang G, Ying R, Huang J, Leskovec J (2020) Multi-hop attention graph neural network. arXiv preprint arXiv:2009.14332","DOI":"10.24963\/ijcai.2021\/425"},{"key":"253_CR27","doi-asserted-by":"crossref","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The world wide web conference, pp 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"253_CR28","doi-asserted-by":"crossref","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the web conference 2020, pp 2331\u20132341","DOI":"10.1145\/3366423.3380297"},{"key":"253_CR29","first-page":"11983","volume":"32","author":"S Yun","year":"2019","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Adv Neural Inf Process Syst 32:11983\u201311993","journal-title":"Adv Neural Inf Process Syst"},{"key":"253_CR30","doi-asserted-by":"crossref","unstructured":"Lv Q, Ding M, Liu Q, Chen Y, Feng W, He S, Zhou C, Jiang J, Dong Y, Tang J (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\u20131160","DOI":"10.1145\/3447548.3467350"},{"key":"253_CR31","doi-asserted-by":"crossref","unstructured":"Hu Z, Dong Y, Wang K, Sun Y (2020) Heterogeneous graph transformer. In: Proceedings of the web conference 2020, pp 2704\u20132710","DOI":"10.1145\/3366423.3380027"},{"issue":"2","key":"253_CR32","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s41019-023-00207-w","volume":"8","author":"L Li","year":"2023","unstructured":"Li L, Duan L, Wang J, He C, Chen Z, Xie G, Deng S, Luo Z (2023) Memory-enhanced transformer for representation learning on temporal heterogeneous graphs. Data Sci Eng 8(2):98\u2013111","journal-title":"Data Sci Eng"},{"key":"253_CR33","doi-asserted-by":"crossref","unstructured":"Jin D, Huo C, Liang C, Yang L (2021) Heterogeneous graph neural network via attribute completion. In: Proceedings of the web conference 2021, pp 391\u2013400","DOI":"10.1145\/3442381.3449914"},{"key":"253_CR34","unstructured":"Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab"},{"key":"253_CR35","unstructured":"Kondor RI, Lafferty J (2002) Diffusion kernels on graphs and other discrete structures. In: Proceedings of the 19th international conference on machine learning, vol 2002, pp 315\u2013322"},{"key":"253_CR36","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in Neural Information Processing Systems 30"},{"key":"253_CR37","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"253_CR38","doi-asserted-by":"crossref","unstructured":"Zhu S, Zhou C, Pan S, Zhu X, Wang B (2019) Relation structure-aware heterogeneous graph neural network. In: 2019 IEEE international conference on data mining (ICDM), IEEE, pp 1534\u20131539","DOI":"10.1109\/ICDM.2019.00203"},{"key":"253_CR39","doi-asserted-by":"crossref","unstructured":"Hong H, Guo H, Lin Y, Yang X, Li Z, Ye J (2020) An attention-based graph neural network for heterogeneous structural learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 4132\u20134139","DOI":"10.1609\/aaai.v34i04.5833"},{"key":"253_CR40","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-024-00253-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-024-00253-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-024-00253-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T02:27:17Z","timestamp":1725848837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-024-00253-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,24]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["253"],"URL":"https:\/\/doi.org\/10.1007\/s41019-024-00253-y","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"value":"2364-1185","type":"print"},{"value":"2364-1541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,24]]},"assertion":[{"value":"3 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"I declare that I have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}