{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:23:19Z","timestamp":1773775399436,"version":"3.50.1"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Major Project for New Generation of AI","award":["2018AAA0100400"],"award-info":[{"award-number":["2018AAA0100400"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076232"],"award-info":[{"award-number":["62076232"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072457"],"award-info":[{"award-number":["62072457"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xiaomi Young Talents Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tip.2023.3293774","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T17:26:33Z","timestamp":1689182793000},"page":"3980-3993","source":"Crossref","is-referenced-by-count":17,"title":["Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7712-2853","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Gaowei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Hongyong","family":"Han","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-0193","authenticated-orcid":false,"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Automation, the Centre for Artificial Intelligence and Robotics (HKISI_CAS), and the State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Chinese Academy of Sciences (CASIA), Beijing, China"}]}],"member":"263","reference":[{"key":"ref13","first-page":"793","article-title":"Link prediction across networks by biased cross-network sampling","author":"qi","year":"2013","journal-title":"Proc IEEE 29th Int Conf Data Eng (ICDE)"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3087830"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref56","first-page":"1081","article-title":"Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation","author":"chen","year":"2019","journal-title":"Proc ICML"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.70"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71246-8_45"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.116"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108638"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00393"},{"key":"ref11","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc NIPS"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3216781"},{"key":"ref10","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","author":"yang","year":"2016","journal-title":"Proc ICML"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"ref17","first-page":"1823","article-title":"Predict anchor links across social networks via an embedding approach","author":"man","year":"2016","journal-title":"Proc IJCAI"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783296"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5692"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995483"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00127"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"ref46","first-page":"14054","article-title":"Local augmentation for graph neural networks","author":"liu","year":"2022","journal-title":"Proc ICML"},{"key":"ref45","first-page":"1","article-title":"DropEdge: Towards deep graph convolutional networks on node classification","author":"rong","year":"2020","journal-title":"Proc ICLR"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"ref42","first-page":"1","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"Proc NIPS"},{"key":"ref41","first-page":"2030","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"ref43","article-title":"Central moment discrepancy (CMD) for domain-invariant representation learning","author":"zellinger","year":"2017","journal-title":"arXiv 1702 08811"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2819980"},{"key":"ref7","first-page":"1895","article-title":"Tri-party deep network representation","author":"pan","year":"2016","journal-title":"Proc IJCAI"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975321.18"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3011807"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3028207"},{"key":"ref6","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"Proc ICLR"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3062692"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2883970"},{"key":"ref80","first-page":"1","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref78","first-page":"1","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"Proc ICLR"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2822283"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.896065"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/438"},{"key":"ref30","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc ICML"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/34.120331"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806501"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2016.10.004"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2018.2850013"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2932096"},{"key":"ref38","first-page":"8713","article-title":"Graph agreement models for semi-supervised learning","author":"stretcu","year":"2019","journal-title":"Proc NeurIPS"},{"key":"ref71","article-title":"BEGAN: Boundary equilibrium generative adversarial networks","author":"berthelot","year":"2017","journal-title":"arXiv 1703 10717"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2842770"},{"key":"ref73","first-page":"3730","article-title":"Joint distribution optimal transportation for domain adaptation","author":"courty","year":"2017","journal-title":"Proc NIPS"},{"key":"ref72","article-title":"Gnu scientific library—Reference manual: Weighted samples","author":"galassi","year":"2011"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3144350"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3156044"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2959834"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557602"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3201529"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/83.931094"},{"key":"ref20","article-title":"Graph transfer learning via adversarial domain adaptation with graph convolution","author":"dai","year":"2019","journal-title":"arXiv 1909 01541"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2636160"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.07.017"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482228"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2010.220","article-title":"Maximum correntropy criterion for robust face recognition","volume":"33","author":"he","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref21","first-page":"27965","article-title":"Shift-robust GNNs: Overcoming the limitations of localized graph training data","author":"zhu","year":"2021","journal-title":"Proc NIPS"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2727278"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_21"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref60","author":"villani","year":"2008","journal-title":"Optimal Transport Old and New"},{"key":"ref62","volume":"523","author":"huber","year":"2004","journal-title":"Robust Statistics"},{"key":"ref61","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc NIPS"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9991910\/10179964.pdf?arnumber=10179964","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T18:35:49Z","timestamp":1691433349000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10179964\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":80,"URL":"https:\/\/doi.org\/10.1109\/tip.2023.3293774","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}