{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T03:32:17Z","timestamp":1768793537007,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"17-18","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["No.2022ZD0119501"],"award-info":[{"award-number":["No.2022ZD0119501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072288"],"award-info":[{"award-number":["62072288"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010029","name":"Taishan Scholar Foundation of Shandong Province","doi-asserted-by":"publisher","award":["tsqn202211154"],"award-info":[{"award-number":["tsqn202211154"]}],"id":[{"id":"10.13039\/501100010029","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022MF268"],"award-info":[{"award-number":["ZR2022MF268"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s10489-024-05567-y","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T07:02:07Z","timestamp":1718866927000},"page":"8073-8091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MHGNN: Multi-view fusion based Heterogeneous Graph Neural Network"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3131-2723","authenticated-orcid":false,"given":"Chao","family":"Li","sequence":"first","affiliation":[]},{"given":"Xiangkai","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yeyu","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Zhongying","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Lingtao","family":"Su","sequence":"additional","affiliation":[]},{"given":"Qingtian","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"issue":"2","key":"5567_CR1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/2481244.2481248","volume":"14","author":"Y Sun","year":"2013","unstructured":"Sun Y, Han J (2013) Mining heterogeneous information networks. ACM SIGKDD Explor Newsletter 14(2):20\u201328. https:\/\/doi.org\/10.1145\/2481244.2481248","journal-title":"ACM SIGKDD Explor Newsletter"},{"issue":"2","key":"5567_CR2","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. https:\/\/doi.org\/10.1109\/TBDATA.2022.3177455","journal-title":"IEEE Trans Big Data"},{"key":"5567_CR3","doi-asserted-by":"publisher","unstructured":"Li C, Liu X, Yan Y, Zhao Z, Zeng Q (2023) Hetgnn-sf: Self-supervised learning on heterogeneous graph neural network via semantic strength and feature similarity. Appl Intell, 1\u201318. https:\/\/doi.org\/10.1007\/s10489-023-04612-6","DOI":"10.1007\/s10489-023-04612-6"},{"key":"5567_CR4","doi-asserted-by":"publisher","first-page":"122404","DOI":"10.1016\/j.eswa.2023.122404","volume":"238","author":"C Li","year":"2024","unstructured":"Li C, Fu J, Yan Y, Zhao Z, Zeng Q (2024) Higher order heterogeneous graph neural network based on node attribute enhancement. Expert Syst Appl 238:122404. https:\/\/doi.org\/10.1016\/j.eswa.2023.122404","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5567_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568022","volume":"1","author":"C Gao","year":"2023","unstructured":"Gao C, Zheng Y, Li N, Li Y, Qin Y, Piao J, Quan Y, Chang J, Jin D, He X et al (2023) A survey of graph neural networks for recommender systems: challenges, methods, and directions. ACM Trans Recommender Syst 1(1):1\u201351. https:\/\/doi.org\/10.1145\/3568022","journal-title":"ACM Trans Recommender Syst"},{"issue":"1","key":"5567_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528667","volume":"41","author":"H Wang","year":"2023","unstructured":"Wang H, Zhou K, Zhao X, Wang J, Wen J-R (2023) Curriculum pre-training heterogeneous subgraph transformer for top-n recommendation. ACM Trans Inf Syst 41(1):1\u201328. https:\/\/doi.org\/10.1145\/3528667","journal-title":"ACM Trans Inf Syst"},{"issue":"3","key":"5567_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18653\/v1\/D19-1488","volume":"39","author":"T Yang","year":"2021","unstructured":"Yang T, Hu L, Shi C, Ji H, Li X, Nie L (2021) Hgat: Heterogeneous graph attention networks for semi-supervised short text classification. ACM Trans Inf Syst 39(3):1\u201329. https:\/\/doi.org\/10.18653\/v1\/D19-1488","journal-title":"ACM Trans Inf Syst"},{"key":"5567_CR8","doi-asserted-by":"publisher","unstructured":"Gao D, Li K, Wang R, Shan S, Chen X (2020) Multi-modal graph neural network for joint reasoning on vision and scene text. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12746\u201312756. https:\/\/doi.org\/10.1109\/cvpr42600.2020.01276","DOI":"10.1109\/cvpr42600.2020.01276"},{"key":"5567_CR9","doi-asserted-by":"publisher","unstructured":"Malekzadeh M, Hajibabaee P, Heidari M, Zad S, Uzuner O, Jones JH (2021) Review of graph neural network in text classification. In: 2021 IEEE 12th Annual ubiquitous computing, electronics & mobile communication conference (UEMCON), pp 0084\u20130091. IEEE. https:\/\/doi.org\/10.1109\/UEMCON53757.2021.9666633","DOI":"10.1109\/UEMCON53757.2021.9666633"},{"key":"5567_CR10","doi-asserted-by":"publisher","unstructured":"Hu L, Xu S, Li C, Yang C, Shi C, Duan N, Xie X, Zhou M (2020) Graph neural news recommendation with unsupervised preference disentanglement. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 4255\u20134264. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.392","DOI":"10.18653\/v1\/2020.acl-main.392"},{"key":"5567_CR11","doi-asserted-by":"publisher","unstructured":"Hou S, Ye Y, Song Y, Abdulhayoglu M (2017) Hindroid: An intelligent android malware detection system based on structured heterogeneous information network. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1507\u20131515. https:\/\/doi.org\/10.1145\/3097983.3098026","DOI":"10.1145\/3097983.3098026"},{"key":"5567_CR12","doi-asserted-by":"publisher","unstructured":"Louis A, Van\u00a0Dijck G, Spanakis G (2023) Finding the law: Enhancing statutory article retrieval via graph neural networks. In: Proceedings of the 17th conference of the european chapter of the association for computational linguistics, pp 2753\u20132768. https:\/\/doi.org\/10.48550\/arXiv.2301.12847","DOI":"10.48550\/arXiv.2301.12847"},{"issue":"6","key":"5567_CR13","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1109\/TNNLS.2021.3114027","volume":"33","author":"C Li","year":"2021","unstructured":"Li C, Peng H, Li J, Sun L, Lyu L, Wang L, Philip SY, He L (2021) Joint stance and rumor detection in hierarchical heterogeneous graph. IEEE Trans Neural Netw Learn Syst 33(6):2530\u20132542. https:\/\/doi.org\/10.1109\/TNNLS.2021.3114027","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5567_CR14","doi-asserted-by":"publisher","first-page":"107842","DOI":"10.1016\/j.knosys.2021.107842","volume":"240","author":"L Qian","year":"2022","unstructured":"Qian L, Wang J, Lin H, Xu B, Yang L (2022) Heterogeneous information network embedding based on multiperspective metapath for question routing. Knowl-Based Syst 240:107842. https:\/\/doi.org\/10.1016\/j.knosys.2021.107842","journal-title":"Knowl-Based Syst"},{"issue":"02","key":"5567_CR15","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1109\/TKDE.2021.3101356","volume":"35","author":"Y Yang","year":"2023","unstructured":"Yang Y, Guan Z, Li J, Zhao W, Cui J, Wang Q (2023) Interpretable and efficient heterogeneous graph convolutional network. IEEE Trans Knowl Data Eng 35(02):1637\u20131650. https:\/\/doi.org\/10.1109\/TKDE.2021.3101356","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5567_CR16","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1145\/3366423.3380297","DOI":"10.1145\/3366423.3380297"},{"key":"5567_CR17","doi-asserted-by":"publisher","unstructured":"Wang X, Liu N, Han H, Shi C (2021) Self-supervised heterogeneous graph neural network with co-contrastive learning. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 1726\u20131736. https:\/\/doi.org\/10.1145\/3447548.3467415","DOI":"10.1145\/3447548.3467415"},{"key":"5567_CR18","doi-asserted-by":"publisher","first-page":"120115","DOI":"10.1016\/j.eswa.2023.120115","volume":"225","author":"Y Yan","year":"2023","unstructured":"Yan Y, Li C, Yu Y, Li X, Zhao Z (2023) Osgnn: Original graph and subgraph aggregated graph neural network. Expert Syst Appl 225:120115. https:\/\/doi.org\/10.1016\/j.eswa.2023.120115","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5567_CR19","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1109\/TKDE.2021.3074654","volume":"35","author":"J Li","year":"2021","unstructured":"Li J, Peng H, Cao Y, Dou Y, Zhang H, Philip SY, He L (2021) Higher-order attribute-enhancing heterogeneous graph neural networks. IEEE Trans Knowl Data Eng 35(1):560\u2013574. https:\/\/doi.org\/10.1109\/TKDE.2021.3074654","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"5567_CR20","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1080\/02626667.2021.2003367","volume":"67","author":"SA Hosseini","year":"2022","unstructured":"Hosseini SA, Abbaszadeh Shahri A, Asheghi R (2022) Prediction of bedload transport rate using a block combined network structure. Hydrol Sci J 67(1):117\u2013128. https:\/\/doi.org\/10.1080\/02626667.2021.2003367","journal-title":"Hydrol Sci J"},{"issue":"5","key":"5567_CR21","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui P, Wang X, Pei J, Zhu W (2018) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833\u2013852. https:\/\/doi.org\/10.1109\/TKDE.2018.2849727","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5567_CR22","doi-asserted-by":"publisher","unstructured":"Lei R, Zhen W, Li Y, Ding B, Wei Z (2022) Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks. In: Advances in neural information processing systems. https:\/\/doi.org\/10.48550\/arXiv.2205.13892","DOI":"10.48550\/arXiv.2205.13892"},{"key":"5567_CR23","doi-asserted-by":"publisher","unstructured":"Bruna J, Zaremba W, Szlam A, Lecun Y (2014) Spectral networks and locally connected networks on graphs. In: International conference on learning representations (ICLR2014), CBLS, April 2014, p. https:\/\/doi.org\/10.48550\/arXiv.1312.6203","DOI":"10.48550\/arXiv.1312.6203"},{"key":"5567_CR24","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"5567_CR25","doi-asserted-by":"publisher","unstructured":"Wang X, Zhang M (2022) How powerful are spectral graph neural networks. In: International conference on machine learning, pp 23341\u201323362. PMLR. https:\/\/doi.org\/10.48550\/arXiv.2205.11172","DOI":"10.48550\/arXiv.2205.11172"},{"key":"5567_CR26","doi-asserted-by":"publisher","unstructured":"Yang L, Chen C, Li W, Niu B, Gu J, Wang C, He D, Guo Y, Cao X (2022) Self-supervised graph neural networks via diverse and interactive message passing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp 4327\u20134336. https:\/\/doi.org\/10.1609\/aaai.v36i4.20353","DOI":"10.1609\/aaai.v36i4.20353"},{"key":"5567_CR27","doi-asserted-by":"publisher","unstructured":"Mo Y, Peng L, Xu J, Shi X, Zhu X (2022) Simple unsupervised graph representation learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 7797\u20137805. https:\/\/doi.org\/10.1609\/aaai.v36i7.20748","DOI":"10.1609\/aaai.v36i7.20748"},{"key":"5567_CR28","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. CorpusID: 4755450"},{"key":"5567_CR29","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"5567_CR30","unstructured":"Chen D, O\u2019Bray L, Borgwardt K (2022) Structure-aware transformer for graph representation learning. In: International conference on machine learning, pp 3469\u20133489. PMLR. CorpusID: 246634635"},{"issue":"3","key":"5567_CR31","doi-asserted-by":"publisher","first-page":"562","DOI":"10.2166\/hydro.2020.098","volume":"22","author":"R Asheghi","year":"2020","unstructured":"Asheghi R, Hosseini SA, Saneie M, Shahri AA (2020) Updating the neural network sediment load models using different sensitivity analysis methods: a regional application. J Hydroinformatics 22(3):562\u2013577. https:\/\/doi.org\/10.2166\/hydro.2020.098","journal-title":"J Hydroinformatics"},{"key":"5567_CR32","doi-asserted-by":"publisher","unstructured":"Guha S, Kodipalli A (2023) Sensitivity analysis of physical and mental health factors affecting polycystic ovary syndrome in women. Expert Syst 13413. https:\/\/doi.org\/10.1111\/exsy.13413","DOI":"10.1111\/exsy.13413"},{"key":"5567_CR33","doi-asserted-by":"publisher","first-page":"105619","DOI":"10.1016\/j.engappai.2022.105619","volume":"118","author":"B Firouzi","year":"2023","unstructured":"Firouzi B, Abbasi A, Sendur P, Zamanian M, Chen H (2023) Enhancing the performance of piezoelectric energy harvester under electrostatic actuation using a robust metaheuristic algorithm. Eng Appl Artif Intell 118:105619. https:\/\/doi.org\/10.1016\/j.engappai.2022.105619","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"5567_CR34","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1007\/s11053-022-10051-w","volume":"31","author":"A Abbaszadeh Shahri","year":"2022","unstructured":"Abbaszadeh Shahri A, Shan C, Larsson S (2022) A novel approach to uncertainty quantification in groundwater table modeling by automated predictive deep learning. Nat Resour Res 31(3):1351\u20131373. https:\/\/doi.org\/10.1007\/s11053-022-10051-w","journal-title":"Nat Resour Res"},{"key":"5567_CR35","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Adv Neural Inf Process Syst32. CorpusID: 202763464"},{"key":"5567_CR36","doi-asserted-by":"publisher","unstructured":"Zhao J, Wang X, Shi C, Hu B, Song G, Ye Y (2021) Heterogeneous graph structure learning for graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4697\u20134705. https:\/\/doi.org\/10.1609\/aaai.v35i5.16600","DOI":"10.1609\/aaai.v35i5.16600"},{"key":"5567_CR37","doi-asserted-by":"publisher","first-page":"119982","DOI":"10.1016\/j.eswa.2023.119982","volume":"224","author":"Z Wang","year":"2023","unstructured":"Wang Z, Yu D, Li Q, Shen S, Yao S (2023) Sr-hgn: Semantic-and relation-aware heterogeneous graph neural network. Expert Syst Appl 224:119982. https:\/\/doi.org\/10.1016\/j.eswa.2023.119982","journal-title":"Expert Syst Appl"},{"key":"5567_CR38","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"5567_CR39","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1145\/3366423.3380297","volume":"170","author":"X Fu","year":"2024","unstructured":"Fu X, King I (2024) Mecch: metapath context convolution-based heterogeneous graph neural networks. Neural Netw 170:266\u2013275. https:\/\/doi.org\/10.1145\/3366423.3380297","journal-title":"Neural Netw"},{"key":"5567_CR40","doi-asserted-by":"publisher","unstructured":"Zhang M, Wang X, Zhu M, Shi C, Zhang Z, Zhou J (2022) Robust heterogeneous graph neural networks against adversarial attacks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 36, pp 4363\u20134370. https:\/\/doi.org\/10.1609\/aaai.v36i4.20357","DOI":"10.1609\/aaai.v36i4.20357"},{"issue":"01","key":"5567_CR41","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1109\/TKDE.2021.3079239","volume":"35","author":"H Ji","year":"2023","unstructured":"Ji H, Wang X, Shi C, Wang B, Philip SY (2023) Heterogeneous graph propagation network. IEEE Trans Knowl Data Eng 35(01):521\u2013532. https:\/\/doi.org\/10.1109\/TKDE.2021.3079239","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5567_CR42","doi-asserted-by":"publisher","unstructured":"Gasteiger J, Bojchevski A, G\u00fcnnemann S (2018) Predict then propagate: graph neural networks meet personalized pagerank. In: International conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.1810.05997","DOI":"10.48550\/arXiv.1810.05997"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05567-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05567-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05567-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T12:28:32Z","timestamp":1723033712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05567-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,20]]},"references-count":42,"journal-issue":{"issue":"17-18","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["5567"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05567-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,20]]},"assertion":[{"value":"26 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"There is the consent of all authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Ethics"}},{"value":"There is the consent of all authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that there is no competing interests.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}