{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T02:00:52Z","timestamp":1778896852945,"version":"3.51.4"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000006","name":"ONR","doi-asserted-by":"publisher","award":["N00014-21-1-4002"],"award-info":[{"award-number":["N00014-21-1-4002"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1109\/tnnls.2021.3110982","type":"journal-article","created":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T01:20:00Z","timestamp":1633137600000},"page":"2406-2415","source":"Crossref","is-referenced-by-count":40,"title":["Cross-Domain Graph Anomaly Detection"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6684-6752","authenticated-orcid":false,"given":"Kaize","family":"Ding","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6043-1764","authenticated-orcid":false,"given":"Kai","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9349-1341","authenticated-orcid":false,"given":"Xuan","family":"Shan","sequence":"additional","affiliation":[{"name":"Kuaishou Technology Company Ltd., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering and the Department of Computer Science, University of Virginia, Charlottesville, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-7904","authenticated-orcid":false,"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc ICML"},{"key":"ref38","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159726"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783370"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_3"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.24"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2618243.2618266"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335388"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-010-0001-0"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/299"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.177"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.191"},{"key":"ref20","first-page":"1","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc NeurIPS"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014951"},{"key":"ref21","article-title":"Hierarchical attention transfer network for cross-domain sentiment classification","author":"li","year":"2018","journal-title":"Proc AAAI"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380219"},{"key":"ref23","first-page":"2030","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2932096"},{"key":"ref25","first-page":"1","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc NeurIPS"},{"key":"ref50","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"tzeng","year":"2014","journal-title":"arXiv 1412 3474"},{"key":"ref51","article-title":"Supervised representation learning: Transfer learning with deep autoencoders","author":"zhuang","year":"2015","journal-title":"Proc IJCAI"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5692"},{"key":"ref53","article-title":"Network transfer learning via adversarial domain adaptation with graph convolution","author":"dai","year":"2019","journal-title":"arXiv 1909 01541"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/606"},{"key":"ref10","first-page":"3536","article-title":"LSDA: Large scale detection through adaptation","author":"hoffman","year":"2014","journal-title":"Proc NeurIPS"},{"key":"ref11","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"collobert","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/488"},{"key":"ref12","article-title":"Domain adaptation for large-scale sentiment classification: A deep learning approach","author":"glorot","year":"2011","journal-title":"Proc ICML"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref15","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"Proc ICLR"},{"key":"ref16","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc ICLR"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411922"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449922"},{"key":"ref4","article-title":"Social spammer detection in microblogging","author":"hu","year":"2013","journal-title":"Proc IJCAI"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290978"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2015.09.005"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref7","article-title":"Deep learning for anomaly detection: A review","author":"pang","year":"2020","journal-title":"arXiv 2007 02500"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159685"},{"key":"ref9","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"2014","journal-title":"arXiv 1409 7495"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371788"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/179"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411979"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13672-6_40"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281280"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290964"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW.2013.6547453"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9786556\/09556511.pdf?arnumber=9556511","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T20:31:13Z","timestamp":1656361873000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9556511\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":54,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3110982","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6]]}}}