{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T22:01:12Z","timestamp":1784757672836,"version":"3.55.0"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF0712300"],"award-info":[{"award-number":["2022YFF0712300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172177"],"award-info":[{"award-number":["62172177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Industry-University-Research Collaboration of Zhuhai","award":["ZH22017001210089PWC"],"award-info":[{"award-number":["ZH22017001210089PWC"]}]},{"name":"Knowledge Innovation Program of Wuhan-Shuguang"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tnnls.2024.3358801","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T14:11:58Z","timestamp":1707228718000},"page":"3464-3478","source":"Crossref","is-referenced-by-count":11,"title":["Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4363-1000","authenticated-orcid":false,"given":"Sichao","family":"Fu","sequence":"first","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4863-5681","authenticated-orcid":false,"given":"Qinmu","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"He","sequence":"additional","affiliation":[{"name":"Platform Operation and Marketing Center, JD Retail, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaorui","family":"Wang","sequence":"additional","affiliation":[{"name":"Platform Operation and Marketing Center, JD Retail, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zou","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Statistics, Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duanquan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Yuan","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0607-1777","authenticated-orcid":false,"given":"Xinge","family":"You","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3322739"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3297607"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084125"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3332335"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098156"},{"key":"ref6","first-page":"18648","article-title":"Variational inference for graph convolutional networks in the absence of graph data and adversarial settings","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Elinas"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449952"},{"key":"ref8","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20335"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512186"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref12","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.08.053"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3450352"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3172903"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441735"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref18","first-page":"76","article-title":"From canonical correlation analysis to self-supervised graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Zhang"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/204"},{"key":"ref20","article-title":"Variational graph auto-encoders","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Workshop Bayesian Deep Learn.","author":"Kipf"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00662"},{"key":"ref22","article-title":"Pre-training graph neural networks for generic structural feature extraction","author":"Hu","year":"2019","journal-title":"arXiv:1905.13728"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107564"},{"key":"ref24","first-page":"22667","article-title":"SLAPS: Self-supervision improves structure learning for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Fatemi"},{"key":"ref25","article-title":"How to understand masked autoencoders","author":"Cao","year":"2022","journal-title":"arXiv:2202.03670"},{"key":"ref26","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Tian"},{"key":"ref27","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Hu"},{"key":"ref28","article-title":"UCI machine learning repository","author":"Asuncion","year":"2007"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2307\/353415"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9614-6"},{"key":"ref31","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton"},{"key":"ref32","first-page":"378","article-title":"Graph-revised convolutional network","volume-title":"Proc. Joint Eur. Conf. Mach. Learn. Knowl. Discovery Databases","author":"Yu"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"ref34","first-page":"13333","article-title":"Diffusion improves graph learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Klicpera"},{"key":"ref35","first-page":"912","article-title":"Semi-supervised learning using Gaussian fields and harmonic functions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref36","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wu"},{"key":"ref37","article-title":"Deep graph infomax","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Velickovic"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_14"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI50040.2020.00154"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"ref43","first-page":"4116","article-title":"Contrastive multi-view representation learning on graphs","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Hassani"},{"key":"ref44","article-title":"Deep graph contrastive representation learning","author":"Zhu","year":"2020","journal-title":"arXiv:2006.04131"},{"key":"ref45","first-page":"30414","article-title":"InfoGCL: Information-aware graph contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xu"},{"key":"ref46","article-title":"Large-scale representation learning on graphs via bootstrapping","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Thakoor"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20748"},{"key":"ref48","first-page":"2111","article-title":"Network representation learning with rich text information","volume-title":"Proc. 24th Int. Joint Conf. Artif. Intell.","author":"Yang"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132967"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/601"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/509"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108230"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108334"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10877690\/10423234.pdf?arnumber=10423234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:39:15Z","timestamp":1764959955000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10423234\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":56,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3358801","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}