{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:21:22Z","timestamp":1792786882566,"version":"4.5.0"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["62076004"],"award-info":[{"award-number":["62076004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2108085Y23"],"award-info":[{"award-number":["2108085Y23"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Provincial Key Research and Development Program","award":["2022i01020014"],"award-info":[{"award-number":["2022i01020014"]}]},{"name":"University Synergy Innovation Program of Anhui Province, China","award":["GXXT-2022-032"],"award-info":[{"award-number":["GXXT-2022-032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Sci. Eng."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tnse.2023.3306545","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T13:16:41Z","timestamp":1692364601000},"page":"750-760","source":"Crossref","is-referenced-by-count":18,"title":["FL-GNNs: Robust Network Representation via Feature Learning Guided Graph Neural Networks"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1272-1596","authenticated-orcid":false,"given":"Beibei","family":"Wang","sequence":"first","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6238-1596","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3374-1941","authenticated-orcid":false,"given":"Chris","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, the Chinese University of  Hong Kong, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"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","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Velikovi","year":"2018"},{"key":"ref3","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Hamilton","year":"2017"},{"key":"ref4","article-title":"Predict then propagate: Graph neural networks meet personalized PageRank","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Klicpera","year":"2019"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3115104"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3201243"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"ref12","first-page":"13272","article-title":"Reliable graph neural networks via robust aggregation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Geisler","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/310"},{"key":"ref14","first-page":"9720","article-title":"Graph neural networks with adaptive residual","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu","year":"2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref16","first-page":"663","article-title":"Robust subspace segmentation by low-rank representation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu","year":"2010"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2016.2555239"},{"key":"ref18","article-title":"Adversarial attacks and defenses on graphs: A review, a tool, and empirical studies","author":"Wei","year":"2020"},{"key":"ref19","article-title":"Adversarial attacks on graph neural networks via meta learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zgner","year":"2019"},{"key":"ref20","first-page":"1115","article-title":"Adversarial attack on graph structured data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dai","year":"2018"},{"key":"ref21","first-page":"7637","article-title":"Robustness of graph neural networks at scale","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Geisler","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3127557"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/550"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01066"},{"key":"ref25","article-title":"Robustgcns: Robust norm graph convolutional networks in the presence of node missing data and large noises","author":"Jiang","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371789"},{"key":"ref27","first-page":"20437","article-title":"Graph information bottleneck","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3070599"},{"key":"ref29","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2015"},{"key":"ref30","article-title":"Variational graph auto-encoders","volume-title":"Proc. NIPS Workshop Bayesian Deep Learn.","author":"Kipf","year":"2016"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014586"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9569-8_10"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref34","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Glorot","year":"2010"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref36","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2019"},{"key":"ref37","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Xu","year":"2018"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/563"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482215"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"ref41","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Hu","year":"2020"},{"key":"ref42","article-title":"Deeprobust: A PyTorch library for adversarial attacks and defenses","author":"Li","year":"2020"},{"key":"ref43","first-page":"2579","article-title":"Visualizing data using T-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Network Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488902\/10382160\/10224352.pdf?arnumber=10224352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T14:21:38Z","timestamp":1734013298000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10224352\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":43,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnse.2023.3306545","relation":{},"ISSN":["2327-4697","2334-329X"],"issn-type":[{"value":"2327-4697","type":"electronic"},{"value":"2334-329X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}