{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:52:29Z","timestamp":1772905949042,"version":"3.50.1"},"reference-count":72,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"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":["61771386"],"award-info":[{"award-number":["61771386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research and Development Program of Shaanxi","award":["2020SF-359"],"award-info":[{"award-number":["2020SF-359"]}]},{"name":"Research and Development Program of Shaanxi","award":["2021JQ-487"],"award-info":[{"award-number":["2021JQ-487"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,6,1]]},"DOI":"10.1109\/jiot.2025.3539478","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T18:49:35Z","timestamp":1738867775000},"page":"17987-18003","source":"Crossref","is-referenced-by-count":3,"title":["High-Order Structure-Preserving Graph Neural Network for Few-Shot Learning"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6191-1102","authenticated-orcid":false,"given":"Guangfeng","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4302-1620","authenticated-orcid":false,"given":"Dan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yindi","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Software Technology, Shaanxi College of Communication Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2537-639X","authenticated-orcid":false,"given":"Xiaobing","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0833-2336","authenticated-orcid":false,"given":"Kaiyang","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6672-7948","authenticated-orcid":false,"given":"Fan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Vinyals"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref3","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Finn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00053"},{"key":"ref6","first-page":"1","article-title":"A closer look at few-shot classification","volume-title":"Proc. 7th Int. Conf. Learn. Represent. (ICLR)","author":"Chen"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00042"},{"key":"ref8","article-title":"A baseline for few-shot image classification","author":"Dhillon","year":"2019","journal-title":"arXiv:1909.02729"},{"key":"ref9","first-page":"5021","article-title":"Learning from the past: Continual meta-learning with Bayesian graph neural networks","volume-title":"Proc. 30th Int. Conf. Artif. Intell. (AAAI)","author":"Luo"},{"key":"ref10","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref11","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. 38th Int. Conf. Mach. Learn. (ICML)","author":"Radford"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00886"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557681"},{"key":"ref14","first-page":"13073","article-title":"Rectifying the shortcut learning of background for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Luo"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00929"},{"key":"ref17","article-title":"Meta-learning across meta-tasks for few-shot learning","author":"Fei","year":"2020","journal-title":"arXiv:2002.04274"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref20","first-page":"721","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Proc. 32nd Int. Conf. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Oreshkin"},{"key":"ref21","first-page":"1","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. 5th Int. Conf. Learn. Represent. (ICLR)","author":"Ravi"},{"key":"ref22","first-page":"1","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. 7th Int. Conf. Learn. Represent. (ICLR)","author":"Rusu"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.06.090"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00236"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3058098"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20226"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3061978"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207213"},{"key":"ref29","article-title":"Attentive graph neural networks for few-shot learning","author":"Cheng","year":"2020","journal-title":"arXiv:2007.06878"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00931"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186116"},{"key":"ref35","article-title":"Relational representation learning for dynamic (knowledge) graphs: A survey","author":"Kazemi","year":"2019","journal-title":"arXiv:1905.11485"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3483595"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/630"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01157"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICME46284.2020.9102726"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.07.014"},{"key":"ref41","article-title":"Deep iterative and adaptive learning for graph neural networks","author":"Chen","year":"2019","journal-title":"arXiv:1912.07832"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108039"},{"key":"ref43","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3370918"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102428"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3389966"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3371592"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03170-7"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3133646"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3116209"},{"key":"ref51","first-page":"153","article-title":"Locality preserving projections","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"He"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2014.01.002"},{"key":"ref53","first-page":"1","article-title":"Few-shot learning with graph neural networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Satorras"},{"key":"ref54","article-title":"Meta-learning for semi-supervised few-shot classification","author":"Ren","year":"2018","journal-title":"arXiv:1803.00676"},{"key":"ref55","first-page":"1","article-title":"Meta-learning with differentiable closed-form solvers","volume-title":"Proc. 7th Int. Conf. Learn. Represent. (ICLR)","author":"Bertinetto"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref57","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01069"},{"key":"ref59","first-page":"2212","article-title":"Parameterless transductive feature re-representation for few-shot learning","volume-title":"Proc. 38th Int. Conf. Mach. Learn. (ICML)","author":"Cui"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02258"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00727"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8070179"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02701"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124676"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110264"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110855"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106289"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/tetci.2024.3483255"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3403865"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"ref71","first-page":"11660","article-title":"Laplacian regularized few-shot learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn. (ICML)","author":"Ziko"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645466"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11008454\/10876174.pdf?arnumber=10876174","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T17:43:11Z","timestamp":1747849391000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10876174\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,1]]},"references-count":72,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3539478","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,1]]}}}