{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:34:25Z","timestamp":1776440065730,"version":"3.51.2"},"reference-count":42,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC Program","doi-asserted-by":"publisher","award":["62272338"],"award-info":[{"award-number":["62272338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022T150470"],"award-info":[{"award-number":["2022T150470"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M702448"],"award-info":[{"award-number":["2021M702448"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tifs.2023.3272731","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T18:44:45Z","timestamp":1683139485000},"page":"2870-2882","source":"Crossref","is-referenced-by-count":25,"title":["Motif-Level Anomaly Detection in Dynamic Graphs"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8705-5191","authenticated-orcid":false,"given":"Zirui","family":"Yuan","sequence":"first","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1830-9797","authenticated-orcid":false,"given":"Minglai","family":"Shao","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0551-2163","authenticated-orcid":false,"given":"Qiben","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1347"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3118815"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICIoT48696.2020.9089465"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3439950"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1126\/science.298.5594.824"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5987"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1126\/science.1089167"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3299886"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220024"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/614"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481955"},{"key":"ref12","article-title":"H-VGRAE: A hierarchical stochastic spatial\u2013temporal embedding method for robust anomaly detection in dynamic networks","author":"Yang","year":"2020","journal-title":"arXiv:2007.06903"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3124061"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/375663.375668"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2612184"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.22"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939783"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220040"},{"key":"ref19","article-title":"Deep learning for anomaly detection: A survey","author":"Chalapathy","year":"2019","journal-title":"arXiv:1901.03407"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422548"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3117842"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2956532"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2566618"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3383583.3398518"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3473911"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052653"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330882"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5340"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.21236\/ADA164453"},{"key":"ref30","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv:1609.02907"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50417-5_22"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"ref33","article-title":"Dynamic graph representation learning via self-attention networks","author":"Sankar","year":"2018","journal-title":"arXiv:1812.09430"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45442-5_53"},{"key":"ref35","article-title":"Do transformers really perform bad for graph representation?","author":"Ying","year":"2021","journal-title":"arXiv preprint arXiv:2106.05234"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref37","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1907.11692"},{"key":"ref39","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilmer"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053387"},{"key":"ref41","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Velickovic"},{"key":"ref42","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9970396\/10115014.pdf?arnumber=10115014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T04:17:50Z","timestamp":1709266670000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10115014\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3272731","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}