{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:18:52Z","timestamp":1777954732472,"version":"3.51.4"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Australian Research Council Discovery Projects","award":["230101445"],"award-info":[{"award-number":["230101445"]}]},{"name":"Australian Research Council Discovery Projects","award":["240101322"],"award-info":[{"award-number":["240101322"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472317"],"award-info":[{"award-number":["62472317"]}],"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":[[2026,6]]},"DOI":"10.1109\/tkde.2026.3676748","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T20:10:37Z","timestamp":1774296637000},"page":"3611-3625","source":"Crossref","is-referenced-by-count":0,"title":["Fairness-Aware Hypergraph Self-Supervised Learning With Sampling-Efficient Signals"],"prefix":"10.1109","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2951-2226","authenticated-orcid":false,"given":"Fan","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3554-3219","authenticated-orcid":false,"given":"Xiaoyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5877-7387","authenticated-orcid":false,"given":"Dawei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4418-7024","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Gongshang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-2600","authenticated-orcid":false,"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-7225","authenticated-orcid":false,"given":"Xuemin","family":"Lin","sequence":"additional","affiliation":[{"name":"Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539473"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2190083"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2957097"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403389"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00036"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3605776"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"ref8","first-page":"1","article-title":"You are AllSet: A multiset function framework for hypergraph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chien","year":"2021"},{"key":"ref9","first-page":"1909","article-title":"Augmentations in hypergraph contrastive learning: Fabricated and generative","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wei","year":"2022"},{"key":"ref10","first-page":"1","article-title":"Bootstrapped representation learning on graphs","volume-title":"Proc. Int. Conf. Learn. Representations Workshop Geometrical Topol. Representation Learn.","author":"Thakoor","year":"2021"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00090"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591737"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3589335.3651493"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26019"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532058"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482426"},{"key":"ref17","first-page":"1247","article-title":"Deep canonical correlation analysis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Andrew","year":"2013"},{"key":"ref18","first-page":"1","article-title":"Learning fair graph representations via automated data augmentations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ling","year":"2023"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645532"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28776"},{"key":"ref21","article-title":"A framework for understanding unintended consequences of machine learning","author":"Suresh","year":"2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.020"},{"key":"ref24","first-page":"4398","article-title":"Hypergraph self-supervised learning with sampling-efficient signals","volume-title":"Proc. Int. Joint Conf. Artif. Intell.","author":"Li","year":"2024"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498391"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671826"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01459"},{"key":"ref28","first-page":"35605","article-title":"From hypergraph energy functions to hypergraph neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang","year":"2023"},{"key":"ref29","first-page":"12087","article-title":"Sheaf hypergraph networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Duta","year":"2023"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i17.34022"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i17.34023"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8986"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00161"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3265598"},{"key":"ref36","first-page":"3323","article-title":"Equality of opportunity in supervised learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hardt","year":"2016"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3649142"},{"key":"ref38","first-page":"2114","article-title":"Towards a unified framework for fair and stable graph representation learning","volume-title":"Proc. Conf. Uncertainty Artif. Intell.","author":"Agarwal","year":"2021"},{"key":"ref39","first-page":"76","article-title":"From canonical correlation analysis to self-supervised graph neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang","year":"2021"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00946"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00990"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512189"},{"key":"ref44","first-page":"879","article-title":"Attenuating bias in word vectors","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Dev","year":"2019"},{"key":"ref45","first-page":"1511","article-title":"HyperGCN: A new method of training graph convolutional networks on hypergraphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Yadati","year":"2019"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557447"},{"key":"ref47","first-page":"2427","article-title":"The total variation on hypergraphs-learning on hypergraphs revisited","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hein","year":"2013"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/267"},{"key":"ref49","first-page":"1","article-title":"Deep graph infomax","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"ref50","article-title":"Deep graph contrastive representation learning","author":"Zhu","year":"2020"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539425"},{"key":"ref52","first-page":"1","article-title":"HypeBoy: Generative self-supervised representation learning on hypergraphs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kim","year":"2024"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0205"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/473"},{"key":"ref55","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref56","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Paszke","year":"2019"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref58","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"You","year":"2020"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26336"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28698"},{"key":"ref62","first-page":"325","article-title":"Learning fair representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zemel","year":"2013"},{"key":"ref63","first-page":"15776","article-title":"Exploring algorithmic fairness in robust graph covering problems","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Rahmattalabi","year":"2019"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403080"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467266"},{"key":"ref66","first-page":"25944","article-title":"Post-processing for individual fairness","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Petersen","year":"2021"},{"key":"ref67","first-page":"715","article-title":"Compositional fairness constraints for graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bose","year":"2019"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539404"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615092"},{"key":"ref71","article-title":"Fair node representation learning via adaptive data augmentation","author":"Kose","year":"2022"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3133818"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709327"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-34048-2_13"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3384181"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2025\/69"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441835"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-021-01784-1"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1145\/3701551.3703504"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11503382\/11454462.pdf?arnumber=11454462","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T19:37:41Z","timestamp":1777923461000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11454462\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":79,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2026.3676748","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":[[2026,6]]}}}