{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T16:26:25Z","timestamp":1755793585584,"version":"3.44.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3584523","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T13:39:16Z","timestamp":1751290756000},"page":"114865-114879","source":"Crossref","is-referenced-by-count":0,"title":["AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5082-7141","authenticated-orcid":false,"given":"Chuan","family":"Dai","sequence":"first","affiliation":[{"name":"School of Intelligent Science and Engineering, Xi’an Peihua University, Xi’an, China"}]},{"given":"Yajuan","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an, China"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xi’an University of Technology, Xi’an, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0524-5926","authenticated-orcid":false,"given":"Zhijie","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-024-01271-4"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tcss.2022.3151822"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-024-05864-w"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.5121\/ijbb.2013.3202"},{"issue":"2","key":"ref5","first-page":"347","article-title":"Performance analysis of graph Laplacian matrices in detecting protein complexes","volume":"6","author":"Yun-Yuan","year":"2012","journal-title":"Int. J. Secur. Appl."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-15392-6_26"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICAC61394.2024.10718756"},{"key":"ref8","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref9","first-page":"1695","article-title":"A unified lottery ticket hypothesis for graph neural networks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref10","first-page":"1","article-title":"You can have better graph neural networks by not training weights at All: Finding untrained GNNs tickets","volume-title":"Proc. 1st Learn. Graphs Conf.","author":"Huang"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3447085"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1090\/cbms\/092"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-15392-6_25"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.88.042813"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127507"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127482"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/cav.2084"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/icme51207.2021.9428116"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3233\/ida-205725"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa598"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad414"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3584371.3612963"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110115"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43418-1_8"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/rs15123172"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2023.1288842"},{"key":"ref27","first-page":"1","article-title":"Not all low-pass filters are robust in graph convolutional networks","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Chang"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00943"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i19.30098"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29246"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3171199"},{"key":"ref32","first-page":"15786","article-title":"Node embedding from neural Hamiltonian orbits in graph neural networks","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Kang"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614933"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i19.30115"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/431"},{"key":"ref36","first-page":"1","article-title":"PairNorm: Tackling oversmoothing in GNNs","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Zhao"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"ref39","first-page":"1","article-title":"Query-driven active surveying for collective classification","volume-title":"Proc. Workshop Mining Learn. Graphs","author":"Namata"},{"key":"ref40","first-page":"1","article-title":"Pruning filters for efficient convnets","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref41","first-page":"1","article-title":"Learning both weights and connections for efficient neural networks","volume-title":"Proc. 29th Annu. Conf. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2023.3334614"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10489-1"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.l803.03635"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"ref46","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Hu"},{"key":"ref47","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref48","first-page":"1","article-title":"To prune, or not to prune: Exploring the efficacy of pruning for model compression","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Zhu"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3390\/s23052452"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3370918"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3196129"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/63"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111340"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11059875.pdf?arnumber=11059875","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T04:45:39Z","timestamp":1755233139000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11059875\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3584523","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2025]]}}}