{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:53Z","timestamp":1775815433127,"version":"3.50.1"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"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. Big Data"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1109\/tbdata.2023.3284270","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T17:43:29Z","timestamp":1686246209000},"page":"1420-1429","source":"Crossref","is-referenced-by-count":9,"title":["RGSE: Robust Graph Structure Embedding for Anomalous Link Detection"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8762-0664","authenticated-orcid":false,"given":"Zhen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongning","family":"Zhang","sequence":"additional","affiliation":[{"name":"54th Research Institute of CETC, Shijiazhuang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9975-9807","authenticated-orcid":false,"given":"Xiaodong","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Management, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00186"},{"key":"ref35","article-title":"Graph-MLP: Node classification without message passing in graph","author":"hu","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403217"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"ref15","first-page":"1188","article-title":"Distributed representations of sentences and documents","author":"le","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"ref14","first-page":"13683","article-title":"Neo-GNNs: Neighborhood overlap-aware graph neural networks for link prediction","author":"yun","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107438"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/614"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220024"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-92719-6"},{"key":"ref10","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"Proc 4th Int Conf Learn Representations"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972818.13"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371788"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2650204"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/299"},{"key":"ref19","first-page":"1","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2015.2391998"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3391298"},{"key":"ref46","first-page":"2909","article-title":"CVXPY: A python-embedded modeling language for convex optimization","volume":"17","author":"diamond","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref23","article-title":"Variational graph auto-encoders","author":"kipf","year":"2016"},{"key":"ref45","first-page":"1","article-title":"Graphsaint: Graph sampling based inductive learning method","author":"zeng","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13672-6_40"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132959"},{"key":"ref20","first-page":"1","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","author":"zong","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.149.1.6611955"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3100889"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.053"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47436-2_45"},{"key":"ref43","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623682"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2014.05.002"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00116"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2849727"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-021-04175-8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053387"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2015.09.003"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.09.038"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/10236926\/10146491.pdf?arnumber=10146491","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:36:13Z","timestamp":1695666973000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10146491\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":46,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2023.3284270","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]}}}