{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:51:32Z","timestamp":1791485492476,"version":"4.3.4"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tpami.2023.3309970","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T17:22:57Z","timestamp":1693416177000},"page":"14856-14871","source":"Crossref","is-referenced-by-count":10,"title":["Label Efficient Regularization and Propagation for Graph Node Classification"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6402-451X","authenticated-orcid":false,"given":"Tian","family":"Xie","sequence":"first","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8736-3012","authenticated-orcid":false,"given":"Rajgopal","family":"Kannan","sequence":"additional","affiliation":[{"name":"U.S. Army Research Lab, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9474-5035","authenticated-orcid":false,"given":"C.-C. Jay","family":"Kuo","sequence":"additional","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","first-page":"1","article-title":"The PageRank citation ranking: Bringing order to the web","author":"page","year":"1999","journal-title":"Proc Web Conf"},{"key":"ref12","first-page":"19665","article-title":"Decoupling the depth and scope of graph neural networks","author":"zeng","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","first-page":"6861","article-title":"Simplifying graph convolutional networks","author":"wu","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","first-page":"1","article-title":"Predict then propagate: Graph neural networks meet personalized PageRank","author":"klicpera","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref53","first-page":"1","article-title":"LMC: Fast training of GNNs via subgraph sampling with provable convergence","author":"shi","year":"2023","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref52","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","author":"zou","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","first-page":"21","article-title":"MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","author":"abu-el-haija","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref55","first-page":"14556","article-title":"Scalable graph neural networks via bidirectional propagation","author":"chen","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","first-page":"1","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref54","first-page":"13354","article-title":"Diffusion improves graph learning","author":"klicpera","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","first-page":"1","article-title":"Simple spectral graph convolution","author":"zhu","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","first-page":"1","article-title":"Combining label propagation and simple models out-performs graph neural networks","author":"huang","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-017-0421-z"},{"key":"ref18","first-page":"1","article-title":"Consistent semi-supervised graph regularization for high dimensional data","volume":"22","author":"mai","year":"2021","journal-title":"J Mach Learn Res"},{"key":"ref51","first-page":"941","article-title":"Stochastic training of graph convolutional networks with variance reduction","volume":"80","author":"chen","year":"2019","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"ref46","first-page":"5708","article-title":"GraphRNN: Generating realistic graphs with deep auto-regressive models","author":"you","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015829"},{"key":"ref47","first-page":"5241","article-title":"GMNN: Graph Markov neural networks","author":"qu","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"scarselli","year":"2009","journal-title":"IEEE Trans Neural Netw"},{"key":"ref41","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"gilmer","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref44","first-page":"1","article-title":"How powerful are graph neural networks?","author":"xu","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref49","first-page":"3281","article-title":"A flexible generative framework for graph-based semi-supervised learning","author":"ma","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref7","first-page":"321","article-title":"Learning with local and global consistency","author":"zhou","year":"2003","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00981"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3171199"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3155478"},{"key":"ref6","first-page":"912","article-title":"Semi-supervised learning using Gaussian fields and harmonic functions","author":"zhu","year":"2003","journal-title":"Proc 20th Int Conf Mach Learn"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref40","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"ref34","first-page":"1","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"zeng","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref37","first-page":"22092","article-title":"Graph random neural networks for semi-supervised learning on graphs","author":"feng","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512044"},{"key":"ref31","first-page":"1","article-title":"Fast graph representation learning with PyTorch geometric","author":"fey","year":"2019","journal-title":"Proc ICLR Workshop Representation Learn Graphs Manifolds"},{"key":"ref30","first-page":"6316","article-title":"Contrastive graph poisson networks: Semi-supervised learning with extremely limited labels","author":"wan","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref33","first-page":"1","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","author":"chen","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref32","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"hu","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref39","first-page":"1","article-title":"Spectral networks and locally connected networks on graphs","author":"bruna","year":"2014","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref38","first-page":"1","article-title":"Columbia object image library (COIL-100)","author":"nene","year":"1996"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0305-8"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref25","first-page":"1","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"2013","journal-title":"Proc Workshop Challenges Representation Learn"},{"key":"ref20","first-page":"1306","article-title":"Poisson learning: Graph based semi-supervised learning at very low label rates","author":"calder","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190672"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3157746"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"ref27","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","author":"yang","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","first-page":"1","article-title":"Pitfalls of graph neural network evaluation","author":"shchur","year":"0","journal-title":"Proc Relational Representation Learn Workshop"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10308548\/10234505.pdf?arnumber=10234505","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T19:54:21Z","timestamp":1701114861000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10234505\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":55,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3309970","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":[[2023,12]]}}}