{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:34:58Z","timestamp":1777613698606,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10489-024-05363-8","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T09:02:57Z","timestamp":1711443777000},"page":"4411-4424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Heterogeneous graph neural network with graph-data augmentation and adaptive denoising"],"prefix":"10.1007","volume":"54","author":[{"given":"Xiaojun","family":"Lou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"5363_CR1","doi-asserted-by":"crossref","unstructured":"Berton L, Valverde-Rebaza J, de\u00a0Andrade\u00a0Lopes A (2015) Link prediction in graph construction for supervised and semi-supervised learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pages 1\u20138. IEEE","DOI":"10.1109\/IJCNN.2015.7280543"},{"key":"5363_CR2","doi-asserted-by":"crossref","unstructured":"Bhagat S, Cormode G,\u00a0Muthukrishnan S (2011) Node classification in social networks. In: Social network data analytics, pages 115\u2013148. Springer,","DOI":"10.1007\/978-1-4419-8462-3_5"},{"key":"5363_CR3","doi-asserted-by":"publisher","first-page":"3913","DOI":"10.1609\/aaai.v36i4.20307","volume":"36","author":"D Bo","year":"2022","unstructured":"Bo D, Hu BB, Wang X, Zhang Z, Shi C, Zhou J (2022) Regularizing graph neural networks via consistency-diversity graph augmentations. Proceedings of the AAAI Conference on Artificial Intelligence 36:3913\u20133921","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5363_CR4","doi-asserted-by":"publisher","first-page":"107611","DOI":"10.1016\/j.knosys.2021.107611","volume":"235","author":"Y Chang","year":"2022","unstructured":"Chang Y, Chen C, Hu W, Zheng Z, Zhou X, Chen S (2022) Megnn: meta-path extracted graph neural network for heterogeneous graph representation learning. Knowledge-Based Systems 235:107611","journal-title":"Knowledge-Based Systems"},{"key":"5363_CR5","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 Sixteenth ACM International Conference on Web Search and Data Mining, pages 544\u2013552","DOI":"10.1145\/3539597.3570484"},{"issue":"1","key":"5363_CR6","first-page":"20","volume":"28","author":"AP Dawid","year":"1979","unstructured":"Dawid AP, Skene AM (1979) Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics) 28(1):20\u201328","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"key":"5363_CR7","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv:1708.04552"},{"key":"5363_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107899","volume":"238","author":"X Dong","year":"2022","unstructured":"Dong X, Zhang Y, Pang K, Chen F, Lu M (2022) Heterogeneous graph neural networks with denoising for graph embeddings. Knowledge-Based Systems 238:107899","journal-title":"Knowledge-Based Systems"},{"key":"5363_CR9","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"key":"5363_CR10","first-page":"2331","volume":"2020","author":"X Fu","year":"2020","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: metapath aggregated graph neural network for heterogeneous graph embedding. Proceedings of The Web Conference 2020:2331\u20132341","journal-title":"Proceedings of The Web Conference"},{"key":"5363_CR11","doi-asserted-by":"crossref","unstructured":"Gong C, Wang D, Li M, Chandra V, Liu Q (2021) Keepaugment: a simple information-preserving data augmentation approach. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 1055\u20131064","DOI":"10.1109\/CVPR46437.2021.00111"},{"key":"5363_CR12","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","volume":"151","author":"P Goyal","year":"2018","unstructured":"Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowledge-Based Systems 151:78\u201394","journal-title":"Knowledge-Based Systems"},{"key":"5363_CR13","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30"},{"key":"5363_CR14","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: Methods and applications. arXiv:1709.05584"},{"issue":"1\u20132","key":"5363_CR15","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/0004-3702(94)00094-8","volume":"82","author":"RJ Hickey","year":"1996","unstructured":"Hickey RJ (1996) Noise modelling and evaluating learning from examples. Artif Intell 82(1\u20132):157\u2013179","journal-title":"Artif Intell"},{"key":"5363_CR16","doi-asserted-by":"publisher","first-page":"4132","DOI":"10.1609\/aaai.v34i04.5833","volume":"34","author":"H Hong","year":"2020","unstructured":"Hong H, Guo H, Lin Y, Yang X, Li Z, Ye J (2020) An attention-based graph neural network for heterogeneous structural learning. Proceedings of the AAAI Conference on Artificial Intelligence 34:4132\u20134139","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5363_CR17","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"5363_CR18","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv:1611.07308"},{"key":"5363_CR19","doi-asserted-by":"crossref","unstructured":"Kunegis J, Lommatzsch A (2009) Learning spectral graph transformations for link prediction. In: Proceedings of the 26th Annual International Conference on Machine Learning, pages 561\u2013568","DOI":"10.1145\/1553374.1553447"},{"key":"5363_CR20","doi-asserted-by":"crossref","unstructured":"Kuo C-W, Ma C-Y, Huang J-B, Kira Z (2020) Featmatch: feature-based augmentation for semi-supervised learning. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16, pages 479\u2013495. Springer","DOI":"10.1007\/978-3-030-58523-5_28"},{"key":"5363_CR21","doi-asserted-by":"crossref","unstructured":"Li J, Peng H, Cao Y, Dou Y, Zhang H, Yu P, He L (2021) Higher-order attribute-enhancing heterogeneous graph neural networks. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2021.3074654"},{"key":"5363_CR22","unstructured":"Lim S, Kim I, Kim T, Kim C, Kim S (2019) Fast autoaugment. Advances in Neural Information Processing Systems 32"},{"key":"5363_CR23","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, pages 1150\u20131160","DOI":"10.1145\/3447548.3467350"},{"key":"5363_CR24","doi-asserted-by":"crossref","unstructured":"Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1105\u20131114","DOI":"10.1145\/2939672.2939751"},{"key":"5363_CR25","doi-asserted-by":"crossref","unstructured":"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, pages 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"5363_CR26","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, van\u00a0den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference, pages 593\u2013607. Springer, 2018","DOI":"10.1007\/978-3-319-93417-4_38"},{"issue":"1","key":"5363_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"5363_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00492-0","volume":"8","author":"C Shorten","year":"2021","unstructured":"Shorten C, Khoshgoftaar TM, Furht B (2021) Text data augmentation for deep learning. J Big Data 8:1\u201334","journal-title":"J Big Data"},{"key":"5363_CR29","first-page":"20","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. Stat 1050:20","journal-title":"Stat"},{"key":"5363_CR30","unstructured":"Verma V, Lamb A, Beckham C, Najafi A, Mitliagkas I, Lopez-Paz D, Bengio Y (2019) Manifold mixup: better representations by interpolating hidden states. In: International conference on machine learning, pages 6438\u20136447. PMLR"},{"key":"5363_CR31","doi-asserted-by":"crossref","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"5363_CR32","doi-asserted-by":"crossref","unstructured":"Wang W, Feng F, He X, Nie L, Chua T-S (2021) Denoising implicit feedback for recommendation. In: Proceedings of the 14th ACM international conference on web search and data mining, pages 373\u2013381","DOI":"10.1145\/3437963.3441800"},{"key":"5363_CR33","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, pages 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"issue":"11","key":"5363_CR34","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1002\/asi.24493","volume":"72","author":"R Xiang","year":"2021","unstructured":"Xiang R, Chersoni E, Lu Q, Huang C-R, Li W, Long Y (2021) Lexical data augmentation for sentiment analysis. J Assoc Inf Sci Technolo 72(11):1432\u20131447","journal-title":"J Assoc Inf Sci Technolo"},{"issue":"4","key":"5363_CR35","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1137\/20M1386062","volume":"63","author":"M Xu","year":"2021","unstructured":"Xu M (2021) Understanding graph embedding methods and their applications. SIAM Rev 63(4):825\u2013853","journal-title":"SIAM Rev"},{"key":"5363_CR36","doi-asserted-by":"crossref","unstructured":"Yang Y, Guan Z, Li J, Zhao W, Cui J, Wang Q (2021) Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2021.3101356"},{"key":"5363_CR37","unstructured":"Ying Z, You J, Morris C, Ren X, Hamilton W, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. Advances in Neural Information Processing Systems 31"},{"key":"5363_CR38","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"},{"key":"5363_CR39","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pages 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"5363_CR40","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Advances in Neural Information Processing Systems, 32"},{"key":"5363_CR41","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, pages 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"5363_CR42","unstructured":"Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Advances in Neural Information Processing Systems, 31"},{"key":"5363_CR43","doi-asserted-by":"crossref","unstructured":"Zhao T, Liu Y, Neves L, Woodford O, Jiang M, Shah N (2021) Data augmentation for graph neural networks. Proceedings of the aaai conference on artificial intelligence 35:11015\u201311023","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"5363_CR44","doi-asserted-by":"publisher","first-page":"13001","DOI":"10.1609\/aaai.v34i07.7000","volume":"34","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. Proceedings of the AAAI conference on artificial intelligence 34:13001\u201313008","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"5363_CR45","doi-asserted-by":"crossref","unstructured":"Zhu P, Yao X,\u00a0Wang Y, Cao M, Hui B, Zhao S, Hu Q (2022) Latent heterogeneous graph network for incomplete multi-view learning. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2022.3154592"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05363-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05363-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05363-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T13:41:39Z","timestamp":1714398099000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05363-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":45,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["5363"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05363-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"24 February 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 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":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}]}}