{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T08:57:17Z","timestamp":1769590637289,"version":"3.49.0"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006099"],"award-info":[{"award-number":["62006099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3394712","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T17:43:07Z","timestamp":1714412587000},"page":"64103-64113","source":"Crossref","is-referenced-by-count":1,"title":["Pseudo Labeling Collaborative Embedding Representation: A Multi-Channel GCNs Framework for Semi-Supervised Node Classification"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3001-056X","authenticated-orcid":false,"given":"Mengnan","family":"Pang","sequence":"first","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9389-7280","authenticated-orcid":false,"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2637-7901","authenticated-orcid":false,"given":"Jingjing","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1290-6112","authenticated-orcid":false,"given":"Pingxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Velickovic"},{"key":"ref2","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref3","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref4","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wu"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.076"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.03.022"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.11.015"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.060"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6783"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25907"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.04.001"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-023-02013-2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121805"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111283"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123520"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102175"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109351"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25856"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.09.035"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.11.027"},{"key":"ref23","first-page":"841","article-title":"N-GCN: Multi-scale graph convolution for semi-supervised node classification","volume-title":"Proc. 35th Uncertainty Artif. Intell. Conf.","author":"Abu-El-Haija"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107616"},{"key":"ref25","article-title":"Adaptive universal generalized pagerank graph neural network","volume-title":"Proc. 9th Int. Conf. Learn. Represent.","author":"Chien"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122685"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122385"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109301"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.03.057"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109799"},{"key":"ref31","first-page":"13366","article-title":"Diffusion improves graph learning","volume-title":"Proc. 33th Int. Conf. Neural Inf. Process. Syst.","volume":"32","author":"Gasteiger"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109850"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103708"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.05.057"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26168"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.02.054"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"ref38","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"3","author":"Lee"},{"key":"ref39","article-title":"In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi- supervised learning","volume-title":"Proc. 9th Int. Conf. Learn. Represent.","author":"Rizve"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.120012"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.09.021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.08.039"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.09.006"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/204"},{"key":"ref45","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10509702.pdf?arnumber=10509702","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T17:28:25Z","timestamp":1715362105000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10509702\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3394712","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}