{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:11:47Z","timestamp":1779379907611,"version":"3.53.1"},"reference-count":61,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533507","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":31,"title":["Semi-supervised Anomaly Detection on Attributed Graphs"],"prefix":"10.1109","author":[{"given":"Atsutoshi","family":"Kumagai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoharu","family":"Iwata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasuhiro","family":"Fujiwara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Dropedge: towards deep graph convolutional networks on node classification","author":"rong","year":"2020","journal-title":"ICLRE"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref33","article-title":"Stochastic blockmodels meet graph neural networks","author":"mehta","year":"2019","journal-title":"ICML"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"ref30","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/488"},{"key":"ref36","author":"paszke","year":"2017","journal-title":"Automatic differentiation in pytorch"},{"key":"ref35","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref34","article-title":"Graph fairing convolutional networks for anomaly detection","author":"mesgaran","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref60","article-title":"Semi-supervised learning using gaussian fields and harmonic functions","author":"zhu","year":"2003","journal-title":"ICML"},{"key":"ref61","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","author":"zong","year":"2018","journal-title":"ICLRE"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975321.18"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358074"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13672-6_40"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.88"},{"key":"ref22","article-title":"Variational graph auto-encoders","author":"kipf","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref21","article-title":"Adam: a method for stochastic optimization","author":"kingma","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref24","article-title":"Survey of fraud detection techniques","author":"kou","year":"2004","journal-title":"ICNSC"},{"key":"ref23","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"ICLRE"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/299"},{"key":"ref25","article-title":"Transfer anomaly detection by inferring latent domain representations","author":"kumagai","year":"2019","journal-title":"NeurIPS"},{"key":"ref50","article-title":"Ocgnn: one-class classification with graph neural networks","author":"wang","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref51","article-title":"Simplifying graph convolutional networks","author":"wu","year":"2019","journal-title":"ICML"},{"key":"ref59","article-title":"Spare: self-paced network representation for few-shot rare category characterization","author":"zhou","year":"2018","journal-title":"KDD"},{"key":"ref58","article-title":"Learning with local and global consistency","author":"zhou","year":"2004","journal-title":"NeurIPS"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref56","article-title":"Revisiting semi-supervised learning with graph embeddings","author":"yang","year":"2016","journal-title":"ICML"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29911-8_50"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281280"},{"key":"ref53","article-title":"Graph inference learning for semi-supervised classification","author":"xu","year":"2020","journal-title":"ICLRE"},{"key":"ref52","article-title":"Imverde: vertex-diminished random walk for learning imbalanced network representation","author":"wu","year":"2018","journal-title":"Big Data"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.11"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412070"},{"key":"ref40","article-title":"Deep one-class classification","author":"ruff","year":"2018","journal-title":"ICML"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/179"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"ref14","first-page":"21","article-title":"Data mining for network intrusion detection","author":"dokas","year":"0","journal-title":"Proc NSF Workshop on next Generation Data Mining"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2015.2479616"},{"key":"ref16","article-title":"Fast graph representation learning with pytorch geometric","author":"fey","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref18","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"NeurIPS"},{"key":"ref19","article-title":"Supervised anomaly detection based on deep autoregressive density estimators","author":"iwata","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref4","article-title":"Integrating network embedding and community outlier detection via multiclass graph description","author":"bandyopadhyay","year":"2020","journal-title":"ECAI"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330112"},{"key":"ref6","article-title":"Deep learning for anomaly detection: A survey","author":"chalapathy","year":"2019","journal-title":"ArXiv"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2420950.2420969"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref7","article-title":"Anomaly detection using one-class neural networks","author":"chalapathy","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref49","article-title":"Graph attention networks","author":"velickovic","year":"2017","journal-title":"ICLRE"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref45","article-title":"Pitfalls of graph neural network evaluation","author":"shchur","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972818.13"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"ref41","article-title":"Deep semi-supervised anomaly detection","author":"ruff","year":"2019","journal-title":"ICLRE"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","location":"Shenzhen, China","start":{"date-parts":[[2021,7,18]]},"end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533507.pdf?arnumber=9533507","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:49Z","timestamp":1652197549000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533507\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533507","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}