{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T06:17:54Z","timestamp":1776233874365,"version":"3.50.1"},"reference-count":85,"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\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515011946"],"award-info":[{"award-number":["2025A1515011946"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Sichuan University"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["SCU2023D008"],"award-info":[{"award-number":["SCU2023D008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"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.2025.3593880","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T18:50:56Z","timestamp":1753901456000},"page":"10129-10141","source":"Crossref","is-referenced-by-count":1,"title":["A Unified Random Walk, Its Induced Laplacians and Spectral Convolutions for Deep Hypergraph Learning"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5196-2726","authenticated-orcid":false,"given":"Jiying","family":"Zhang","sequence":"first","affiliation":[{"name":"EPFL, Lausanne, Switzerland"}]},{"given":"Fuyang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1521-9542","authenticated-orcid":false,"given":"Xi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Beijing, China"}]},{"given":"Guanzi","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0106-8376","authenticated-orcid":false,"given":"Tingyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Alibaba Group, DAMO Academy, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7387-302X","authenticated-orcid":false,"given":"Yu","family":"Rong","sequence":"additional","affiliation":[{"name":"Alibaba Group, DAMO Academy, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9548-1227","authenticated-orcid":false,"given":"Junzhou","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Texas, Arlington, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2368-4084","authenticated-orcid":false,"given":"Yatao","family":"Bian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/518"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0932"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.37"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnaa028"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3039374"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45650-3_22"},{"key":"ref8","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":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747687"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0205"},{"key":"ref13","first-page":"1172","article-title":"Random walks on hypergraphs with edge-dependent vertex weights","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Chitra","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.101.022308"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1088\/2632-072X\/abe27e"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s00026-005-0237-z"},{"key":"ref17","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa003"},{"key":"ref19","article-title":"Edge contraction pooling for graph neural networks","author":"Diehl","year":"2019"},{"key":"ref20","article-title":"Hypergraph convolutional networks via equivalency between hypergraphs and undirected graphs","author":"Zhang","year":"2022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143847"},{"key":"ref22","article-title":"HNHN: Hypergraph networks with hyperedge neurons","author":"Dong","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/353"},{"key":"ref24","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":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3182052"},{"key":"ref26","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilmer","year":"2017"},{"key":"ref27","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":"ref28","article-title":"Equivariant hypergraph diffusion neural operators","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wang","year":"2023"},{"key":"ref29","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Velivckovi\u2019c","year":"2018"},{"key":"ref30","article-title":"Diversified multiscale graph learning with graph self-correction","author":"Chen","year":"2021"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"ref33","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Defferrard","year":"2016"},{"key":"ref34","article-title":"Simple spectral graph convolution","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhu","year":"2021"},{"key":"ref35","first-page":"6437","article-title":"Training graph neural networks with 1000 layers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2021"},{"key":"ref36","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Klicpera","year":"2018"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21286-4_2"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-020-00231-0"},{"key":"ref39","first-page":"4026","article-title":"Re-revisiting learning on hypergraphs: Confidence interval and subgradient method","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang","year":"2017"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2019.03.032"},{"key":"ref41","article-title":"Inhomogeneous hypergraph clustering with applications","author":"Li","year":"2017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412034"},{"key":"ref43","first-page":"2427","article-title":"The total variation on hypergraphs-learning on hypergraphs revisited","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"26","author":"Hein","year":"2013"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/439"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.11.025"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219829"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540012"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.46300\/9109.2021.15.2"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2013.10.012"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7916450"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1090\/cbms\/092"},{"key":"ref52","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2019"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"issue":"9","key":"ref54","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"Leman","year":"1968","journal-title":"Nauchno-Technicheskaya Informatsiya"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00035"},{"key":"ref56","first-page":"652","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Qi","year":"2017"},{"key":"ref57","first-page":"5105","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Qi","year":"2017"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00166"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00167"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00319"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-54605-1_9"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72775-7_13"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2025.3594749"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/DSW.2018.8439897"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2999520"},{"key":"ref68","article-title":"Spectral-based graph convolutional network for directed graphs","author":"Ma","year":"2019"},{"key":"ref69","article-title":"Directed graph convolutional network","author":"Tong","year":"2020"},{"key":"ref70","article-title":"Pairnorm: Tackling oversmoothing in GNNs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhao","year":"2020"},{"key":"ref71","article-title":"Preventing over-smoothing for hypergraph neural networks","author":"Chen","year":"2022"},{"key":"ref72","article-title":"Hypersage: Generalizing inductive representation learning on hypergraphs","author":"Arya","year":"2020"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00669"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx780"},{"key":"ref77","first-page":"9689","article-title":"Evaluating protein transfer learning with ATPE","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Rao","year":"2019"},{"key":"ref78","article-title":"Learning protein sequence embeddings using information from structure","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bepler","year":"2019"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23303-9"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa714"},{"key":"ref81","article-title":"Intrinsic-extrinsic convolution and pooling for learning on 3D protein structures","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hermosilla","year":"2021"},{"key":"ref82","article-title":"Contrastive representation learning for 3D protein structures","author":"Hermosilla","year":"2022"},{"key":"ref83","article-title":"Learning hierarchical protein representations via complete 3D graph networks","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wang","year":"2023"},{"key":"ref84","article-title":"Protein representation learning by geometric structure pretraining","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Zhang","year":"2023"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i17.34022"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11192800\/11103747.pdf?arnumber=11103747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T05:37:00Z","timestamp":1776231420000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11103747\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":85,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3593880","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]]}}}