{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:35:15Z","timestamp":1773725715072,"version":"3.50.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2021YFB2104802"],"award-info":[{"award-number":["2021YFB2104802"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272023"],"award-info":[{"award-number":["62272023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71901011"],"award-info":[{"award-number":["71901011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51991395"],"award-info":[{"award-number":["51991395"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2023,11,1]]},"DOI":"10.1109\/tkde.2022.3231660","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T18:39:55Z","timestamp":1671820795000},"page":"11529-11540","source":"Crossref","is-referenced-by-count":15,"title":["Label-Enhanced Graph Neural Network for Semi-Supervised Node Classification"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4908-3199","authenticated-orcid":false,"given":"Le","family":"Yu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0157-1716","authenticated-orcid":false,"given":"Leilei","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0975-2367","authenticated-orcid":false,"given":"Bowen","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8948-3103","authenticated-orcid":false,"given":"Tongyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7061-9509","authenticated-orcid":false,"given":"Weifeng","family":"Lv","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Development Environment, Beihang University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"ref2","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref4","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref6","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Velickovic"},{"key":"ref7","article-title":"Bag of tricks of semi-supervised classification with graph neural networks","author":"Wang","year":"2021"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/214"},{"key":"ref9","article-title":"HighwayGraph: Modelling long-distance node relations for improving general graph neural network","author":"Chen","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533748"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/563"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3490478"},{"key":"ref13","first-page":"321","article-title":"Learning with local and global consistency","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref14","first-page":"912","article-title":"Semi-supervised learning using gaussian fields and harmonic functions","volume-title":"Proc. Mach. Learn. Proc. 20th Int. Conf.","author":"Zhu"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref20","article-title":"Effective semi-supervised node classification on few-labeled graph data","author":"Zhou","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"ref22","article-title":"Scalable and adaptive graph neural networks with self-label-enhanced training","author":"Sun","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2481244.2481248"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16600"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3079239"},{"key":"ref30","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3160208"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1310.4546"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330785"},{"key":"ref36","first-page":"5754","article-title":"Xlnet: Generalized autoregressive pretraining for language understanding","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref37","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref38","article-title":"SGDR: Stochastic gradient descent with warm restarts","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Loshchilov"},{"issue":"1","key":"ref39","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref43","article-title":"Deep graph library: Towards efficient and scalable deep learning on graphs","author":"Wang","year":"2019"},{"key":"ref44","article-title":"DeeperGCN: All you need to train deeper GCNs","author":"Li","year":"2020"},{"key":"ref45","first-page":"339","article-title":"GAAN: Gated attention networks for learning on large and spatiotemporal graphs","volume-title":"Proc. 34th Conf. Uncertainty Artif. Intell.","author":"Zhang"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref47","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref48","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref49","article-title":"Combining label propagation and simple models out-performs graph neural networks","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Huang"},{"key":"ref50","article-title":"SIGN: Scalable inception graph neural networks","author":"Rossi","year":"2020"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"ref53","article-title":"R-GSN: The relation-based graph similar network for heterogeneous graph","author":"Wu","year":"2021"},{"key":"ref54","article-title":"Hybrid micro\/macro level convolution for heterogeneous graph learning","author":"Yu","year":"2020"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00203"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5833"},{"issue":"Nov","key":"ref57","first-page":"2579","article-title":"Visualizing data using T-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10273671\/09997579.pdf?arnumber=9997579","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T05:28:47Z","timestamp":1716528527000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9997579\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":57,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2022.3231660","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]}}}