{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:59:13Z","timestamp":1775325553467,"version":"3.50.1"},"reference-count":92,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"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":["62037001"],"award-info":[{"award-number":["62037001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20473"],"award-info":[{"award-number":["U21A20473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172370"],"award-info":[{"award-number":["62172370"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176244"],"award-info":[{"award-number":["62176244"]}],"id":[{"id":"10.13039\/501100001809","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":[[2023,3,1]]},"DOI":"10.1109\/tpami.2022.3183143","type":"journal-article","created":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T20:08:02Z","timestamp":1655323682000},"page":"2751-2768","source":"Crossref","is-referenced-by-count":116,"title":["Are Graph Convolutional Networks With Random Weights Feasible?"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1371-2608","authenticated-orcid":false,"given":"Changqin","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1218-2804","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-5694","authenticated-orcid":false,"given":"Feilong","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Sciences, China Jiliang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5256-210X","authenticated-orcid":false,"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[{"name":"HUTECH University, Ho Chi Minh City, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-0351","authenticated-orcid":false,"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Link2Do Technology Ltd, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2883970"},{"key":"ref7","article-title":"Spectral networks and locally connected networks on graphs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bruna"},{"key":"ref8","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref9","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Defferrard"},{"key":"ref10","first-page":"941","article-title":"Stochastic training of graph convolutional networks with variance reduction","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref11","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.04.028"},{"key":"ref14","article-title":"Graph wavelet neural network","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref15","article-title":"LanczosNet: Multi-scale deep graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liao"},{"key":"ref16","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref17","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107"},{"key":"ref18","first-page":"1993","article-title":"Diffusion-convolutional neural networks","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Atwood"},{"key":"ref19","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref21","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Gilmer"},{"key":"ref22","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref23","first-page":"6878","article-title":"Bandit samplers for training graph neural networks","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Liu"},{"key":"ref24","first-page":"11249","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Zou"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/569"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3026079"},{"key":"ref27","article-title":"Dimensional reweighting graph convolutional networks","author":"Zou","year":"2019"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.74"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.068"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105578"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330956"},{"key":"ref33","first-page":"15387","article-title":"Understanding the representation power of graph neural networks in learning graph topology","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Dehmamy"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1200"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.040"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/72.471375"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0847-4_6"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.12.007"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2734043"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489695"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2925883"},{"key":"ref42","article-title":"SIGN: Scalable inception graph neural networks","author":"Rossi","year":"2020"},{"key":"ref43","first-page":"974","article-title":"LazySVD: Even faster SVD decomposition yet without agonizing pain","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Allen-Zhu"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.09.002"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20042"},{"key":"ref46","first-page":"131","article-title":"Regularized least-squares classification","volume":"190","author":"Rifkin","year":"2003","journal-title":"Nato Sci. Ser. Sub Ser. III Comput. Syst. Sci."},{"key":"ref47","first-page":"1313","article-title":"Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning","volume-title":"Proc. 21st Int. Conf. Neural Inf. Process. Syst.","author":"Rahimi"},{"key":"ref48","volume-title":"Sobolev Spaces","volume":"140","author":"Adams","year":"2003"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(96)00000-7"},{"key":"ref50","first-page":"499","article-title":"Stability and generalization","volume":"2","author":"Bousquet","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref51","first-page":"55","article-title":"Stability of randomized learning algorithms","volume":"6","author":"Elisseeff","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref52","first-page":"275","article-title":"Almost-everywhere algorithmic stability and generalization error","volume-title":"Proc. 18th Conf. Uncertainty Artif. Intell.","author":"Kutin"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCT.2012.6511323"},{"key":"ref54","article-title":"Extensions to McDiarmids inequality when differences are bounded with high probability","author":"Kutin","year":"2002"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.2307\/1267352"},{"issue":"7","key":"ref57","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/TNNLS.2015.2512563","article-title":"Image super-resolution via adaptive $\\ell p(0< p< 1)$\u2113p(0<p<1) regularization and sparse representation","volume":"27","author":"Cao","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref58","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v24i1.7519"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2726981"},{"key":"ref63","first-page":"912","article-title":"Semi-supervised learning using gaussian fields and harmonic functions","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Zhu"},{"issue":"Nov","key":"ref64","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_34"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref67","first-page":"496","article-title":"Link-based classification","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Lu"},{"issue":"Nov","key":"ref68","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref69","article-title":"GraphTSNE: A visualization technique for graph-structured data","volume-title":"Proc. ICLR Workshop Representation Learn. Graphs Manifolds","author":"Leow"},{"key":"ref70","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref71","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3174515"},{"key":"ref75","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref76","article-title":"Fast graph representation learning with PyTorch geometric","author":"Fey","year":"2019"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1515\/9781400841356.38"},{"key":"ref78","article-title":"Adaptive graph diffusion networks with hop-wise attention","author":"Sun","year":"2020"},{"key":"ref79","article-title":"GraphSAINT: Graph sampling based inductive learning method","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zeng"},{"key":"ref80","article-title":"DropEdge: Towards deep graph convolutional networks on node classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rong"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00220"},{"key":"ref83","first-page":"6437","article-title":"Training graph neural networks with 1000 layers","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref84","first-page":"3294","article-title":"GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Fey"},{"key":"ref85","first-page":"6733","article-title":"VQ-GNN: A universal framework to scale up graph neural networks using vector quantization","volume-title":"Proc. 35th Int. Conf. Neural Inf. Process. Syst.","author":"Ding"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.3029762"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3032189"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3016143"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.2999032"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3011866"},{"issue":"272","key":"ref91","first-page":"1","article-title":"Transferability of spectral graph convolutional neural networks","volume":"22","author":"Levie","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref92","first-page":"1702","article-title":"Graphon neural networks and the transferability of graph neural networks","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Ruiz"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10036240\/09796468.pdf?arnumber=9796468","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:18:10Z","timestamp":1719407890000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9796468\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":92,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3183143","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":[[2023,3,1]]}}}