{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:48:25Z","timestamp":1772905705123,"version":"3.50.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20252"],"award-info":[{"award-number":["U24A20252"]}],"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":["62327808"],"award-info":[{"award-number":["62327808"]}],"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":["623B2066"],"award-info":[{"award-number":["623B2066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing National Natural Science Funds of China","award":["62021002"],"award-info":[{"award-number":["62021002"]}]},{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University","award":["HMHAI-202412"],"award-info":[{"award-number":["HMHAI-202412"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China"},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2024PT-ZCK-66"],"award-info":[{"award-number":["2024PT-ZCK-66"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2024CY2-GJHX-48"],"award-info":[{"award-number":["2024CY2-GJHX-48"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tpami.2025.3594487","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T18:31:16Z","timestamp":1753986676000},"page":"10160-10176","source":"Crossref","is-referenced-by-count":1,"title":["Self-Supervised Hypergraph Training Framework via Structure-Aware Learning"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0878-2986","authenticated-orcid":false,"given":"Yifan","family":"Feng","sequence":"first","affiliation":[{"name":"School of Software, BNRist, THUIBCS, BLBCI, Tsinghua University, Beijing, China"}]},{"given":"Shiquan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9423-0146","authenticated-orcid":false,"given":"Shihui","family":"Ying","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7092-0596","authenticated-orcid":false,"given":"Shaoyi","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0597-1426","authenticated-orcid":false,"given":"Zongze","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4971-590X","authenticated-orcid":false,"given":"Yue","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, BNRist, THUIBCS, BLBCI, Tsinghua University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-00080-0"},{"key":"ref2","volume-title":"Introduction to Graph Theory","volume":"2","author":"West","year":"2001"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0205"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3182052"},{"key":"ref6","first-page":"1511","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yadati","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/353"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1137\/21m1414024"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnae013"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-24584-w"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202206151"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102828"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72069-7_21"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01784-1"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2024.06.011"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"ref17","article-title":"CERT: Contrastive self-supervised learning for language understanding","author":"Fang","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"ref19","article-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref21","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Brown","year":"2020"},{"key":"ref22","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref23","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref24","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton","year":"2017"},{"key":"ref25","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2023.04.002"},{"key":"ref27","first-page":"17007","article-title":"Dynamic bottleneck for robust self-supervised exploration","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bai","year":"2021"},{"key":"ref28","first-page":"1909","article-title":"Augmentations in hypergraph contrastive learning: Fabricated and generative","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wei","year":"2022"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26019"},{"key":"ref30","article-title":"HypeBoy: Generative self-supervised representation learning on hypergraphs","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Kim","year":"2024"},{"key":"ref31","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"You","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"ref33","article-title":"Pre-trained hypergraph convolutional neural networks with self-supervised learning","volume":"2835\u20138856","author":"Deng","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-022-1410-8"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s41468-023-00142-9"},{"key":"ref36","article-title":"HNHN: Hypergraph networks with hyperedge neurons","author":"Dong","year":"2020"},{"key":"ref37","article-title":"Hypersage: Generalizing inductive representation learning on hypergraphs","author":"Arya","year":"2020"},{"key":"ref38","article-title":"You are allset: A multiset function framework for hypergraph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chien","year":"2022"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i17.34023"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i17.34022"},{"issue":"9","key":"ref41","first-page":"2539","article-title":"Weisfeiler-Lehman graph kernels","volume":"12","author":"Shervashidze","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/34.862201"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1093\/imaiai\/iaz026"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.4064\/am-16-1-47-57"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995387"},{"key":"ref46","first-page":"17559","article-title":"Co-optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Titouan","year":"2020"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539473"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482426"},{"key":"ref49","article-title":"LightHGNN: Distilling hypergraph neural networks into MLPs for 100x faster inference","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Feng","year":"2024"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s00799-015-0156-0"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583379"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599546"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570404"},{"key":"ref57","article-title":"Deep graph infomax","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"ref58","first-page":"4116","article-title":"Contrastive multi-view representation learning on graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hassani","year":"2020"},{"key":"ref59","first-page":"76","article-title":"From canonical correlation analysis to self-supervised graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang","year":"2021"},{"key":"ref60","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref61","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2017"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11192800\/11106277.pdf?arnumber=11106277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T17:36:44Z","timestamp":1759772204000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11106277\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":64,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3594487","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}