{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:58:31Z","timestamp":1784300311219,"version":"3.55.0"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476245"],"award-info":[{"award-number":["62476245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Zhejiang Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LTGG23F030005"],"award-info":[{"award-number":["LTGG23F030005"]}],"id":[{"id":"10.13039\/501100001809","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":[[2025,9]]},"DOI":"10.1109\/tnnls.2025.3569526","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T13:30:26Z","timestamp":1748525426000},"page":"16840-16853","source":"Crossref","is-referenced-by-count":8,"title":["Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2834-2873","authenticated-orcid":false,"given":"Yuanchen","family":"Bei","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3645-1041","authenticated-orcid":false,"given":"Sheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinke","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haishuai","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1097-2044","authenticated-orcid":false,"given":"Jiajun","family":"Bu","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3102609"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788606"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00070"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612064"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM58522.2023.00010"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622568"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/fi12100177"},{"key":"ref10","first-page":"1","article-title":"GNN-based graph anomaly detection with graph anomaly loss","volume-title":"Proc. 2nd Int. Workshop Deep Learn. Graphs: Methods Appl.","author":"Zhao"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482195"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3389714"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"ref14","article-title":"Is homophily a necessity for graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ma"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512069"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119326"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/330"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/270"},{"key":"ref19","first-page":"21076","article-title":"Rethinking graph neural networks for anomaly detection","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tang"},{"key":"ref20","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref21","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref24","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"kovi\u0107"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.06.075"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3580516"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645517"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.690049"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab340"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2980765.2980767"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3439729"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281280"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412070"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3015098"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053387"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498389"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3488375"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3068344"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482057"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3312655"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20340"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3490478"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3257325"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3267902"},{"key":"ref50","first-page":"21","article-title":"MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Abu-El-Haija"},{"key":"ref51","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref52","first-page":"20887","article-title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lim"},{"key":"ref53","first-page":"13242","article-title":"Finding global homophily in graph neural networks when meeting heterophily","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref54","article-title":"How powerful are graph neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1080\/10408340500526766"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635767"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411979"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"ref61","article-title":"Variational graph auto-encoders","volume-title":"Proc. NIPS Workshop Bayesian Deep Learn.","author":"Kipf"},{"key":"ref62","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref63","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref64","first-page":"27021","article-title":"BOND: Benchmarking unsupervised outlier node detection on static attributed graphs","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track","author":"Liu"},{"issue":"86","key":"ref65","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":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539418"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11151745\/11017687.pdf?arnumber=11017687","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:39:44Z","timestamp":1764959984000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11017687\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":66,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2025.3569526","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}