{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:48:45Z","timestamp":1773154125578,"version":"3.50.1"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Korea Government","doi-asserted-by":"publisher","award":["NRF-2018R1C1B5086441"],"award-info":[{"award-number":["NRF-2018R1C1B5086441"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Korea Government","doi-asserted-by":"publisher","award":["2018R1A2B3001628"],"award-info":[{"award-number":["2018R1A2B3001628"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2983987","type":"journal-article","created":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T03:20:22Z","timestamp":1585624822000},"page":"64356-64365","source":"Crossref","is-referenced-by-count":7,"title":["Anomaly Detection by Learning Dynamics From a Graph"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-5487","authenticated-orcid":false,"given":"Jaekoo","family":"Lee","sequence":"first","affiliation":[]},{"given":"Ho","family":"Bae","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2367-197X","authenticated-orcid":false,"given":"Sungroh","family":"Yoon","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1921632.1921634"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1086\/210318"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.92.118701"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-8733(01)00038-7"},{"key":"ref37","article-title":"Inference in probabilistic graphical models by graph neural networks","author":"yoon","year":"2018","journal-title":"arXiv 1803 07710"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.5948\/UPO9781614440222"},{"key":"ref35","first-page":"1","article-title":"Graph based representation and analysis of text document: A survey of techniques","volume":"96","author":"sonawane","year":"2014","journal-title":"Int J Comput Appl"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339723"},{"key":"ref28","article-title":"A novel anomaly detection scheme based on principal component classifier","author":"shyu","year":"2003"},{"key":"ref27","first-page":"387","article-title":"Statistical techniques in anomaly intrusion detection system","volume":"5","author":"om","year":"2012","journal-title":"Int J Adv Eng Technol"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1030194.1015492"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6045-0"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2001.1711"},{"key":"ref20","author":"murphy","year":"2012","journal-title":"Machine Learning A Probabilistic Perspective"},{"key":"ref22","author":"whitman","year":"2011","journal-title":"Principles of Information Security"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/070710111"},{"key":"ref24","article-title":"Graph2vec: Learning distributed representations of graphs","author":"narayanan","year":"2017","journal-title":"arXiv 1707 05005"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jlp.2016.01.024"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.14257\/ijsia.2014.8.2.09"},{"key":"ref50","article-title":"How to construct deep recurrent neural networks","author":"pascanu","year":"2013","journal-title":"arXiv 1312 6026"},{"key":"ref51","article-title":"Regularizing and optimizing LSTM language models","author":"merity","year":"2017","journal-title":"arXiv 1708 02182"},{"key":"ref56","first-page":"5165","article-title":"Link prediction based on graph neural networks","author":"zhang","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref55","article-title":"Dynamic graph convolutional networks","author":"manessi","year":"2017","journal-title":"arXiv 1704 06199"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.03.074"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973440.33"},{"key":"ref52","article-title":"Training recurrent neural networks","author":"sutskever","year":"2013"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.123"},{"key":"ref11","article-title":"Matrix completion on graphs","author":"kalofolias","year":"2014","journal-title":"arXiv 1408 1717"},{"key":"ref40","first-page":"173","article-title":"Deep speech 2: End-to-end speech recognition in English and Mandarin","author":"amodei","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","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":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref14","article-title":"A comprehensive survey on graph neural networks","author":"wu","year":"2019","journal-title":"arXiv 1901 00596"},{"key":"ref15","article-title":"Spectral networks and locally connected networks on graphs","author":"bruna","year":"2013","journal-title":"arXiv 1312 6203"},{"key":"ref16","first-page":"3837","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":"ref17","first-page":"2154","article-title":"Transfer learning for deep learning on graph-structured data","author":"lee","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref18","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.18"},{"key":"ref3","article-title":"An ensemble approach for event detection and characterization in dynamic graphs","author":"rayana","year":"2014","journal-title":"Proc 2nd ACM SIGKDD Workshop Outlier Detection Description Under Data Diversity (ODD)"},{"key":"ref6","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","author":"duvenaud","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","article-title":"MolGAN: An implicit generative model for small molecular graphs","author":"de cao","year":"2018","journal-title":"arXiv 1805 11973"},{"key":"ref8","first-page":"236","article-title":"Understanding belief propagation and its generalizations","volume":"8","author":"yedidia","year":"2003","journal-title":"Exploring Artificial Intelligence in the New Millennium"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/511446.511513"},{"key":"ref49","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref9","article-title":"The PageRank citation ranking: Bringing order to the Web","author":"page","year":"1999"},{"key":"ref46","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"arXiv 1301 3781 [cs]"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.03.022"},{"key":"ref47","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of go with deep neural networks and tree search","volume":"529","author":"silver","year":"2016","journal-title":"Nature"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.364"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1177\/0278364914549607"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09050542.pdf?arnumber=9050542","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:56:17Z","timestamp":1642002977000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9050542\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2983987","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}