{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:30:08Z","timestamp":1774420208248,"version":"3.50.1"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"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":[[2025,4,6]]},"DOI":"10.1109\/icassp49660.2025.10890733","type":"proceedings-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T13:52:43Z","timestamp":1741787563000},"page":"1-5","source":"Crossref","is-referenced-by-count":1,"title":["Wasserstein Heterogeneous Graph Neural Networks for Uncertainty-Aware Anomaly Detection"],"prefix":"10.1109","author":[{"given":"Chen","family":"Chen","sequence":"first","affiliation":[{"name":"Beihang University,School of Cyber Science and Technology,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunchun","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University,School of Computer Science and Engineering,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boxuan","family":"Jiao","sequence":"additional","affiliation":[{"name":"Beihang University,School of Cyber Science and Technology,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guorui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beihang University,School of Computer Science and Engineering,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University,Key Laboratory of Networking,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102152"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2816564"},{"key":"ref4","article-title":"Graph attention networks","volume-title":"ICLR","author":"Veli\u010dkovi\u0107"},{"key":"ref5","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"ICLR","author":"Kipf"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.33969\/J-NaNA.2023.030403"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-6187-0_53"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5409"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498389"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482195"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357820"},{"key":"ref12","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"NIPS","author":"Hamilton"},{"key":"ref13","article-title":"Deep graph infomax","volume-title":"ICLR","author":"Veli\u010dkovi\u0107"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-030718-104938"},{"key":"ref19","article-title":"Stochastic optimization for large-scale optimal transport","volume":"29","author":"Genevay","year":"2016","journal-title":"Advances in neural information processing systems"},{"issue":"1-40","key":"ref20","first-page":"2","article-title":"Optimal transport for domain adaptation","volume":"1","author":"Flamary","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref21","article-title":"Supervised word mover\u2019s distance","volume":"29","author":"Huang","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref22","article-title":"Learning from uncertain curves: The 2- wasserstein metric for gaussian processes","volume":"30","author":"Mallasto","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref23","article-title":"Robust graph learning under wasserstein uncertainty","author":"Zhang","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tcss.2024.3422074"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2023.3252079"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635767"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2481244.2481248"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1089\/big.2020.0062"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330112"},{"key":"ref31","article-title":"Variational graph auto-encoders","author":"Kipf","year":"2016"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371788"},{"issue":"141","key":"ref33","first-page":"1","article-title":"PyGOD: A Python library for graph outlier detection","volume-title":"Journal of Machine Learning Research","volume":"25","author":"Liu","year":"2024"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3015098"},{"key":"ref36","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"CoRR","author":"Paszke","year":"2019"},{"key":"ref37","article-title":"Fast graph representation learning with PyTorch Geometric","volume-title":"ICLR Workshop on Representation Learning on Graphs and Manifolds","author":"Fey"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"}],"event":{"name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Hyderabad, India","start":{"date-parts":[[2025,4,6]]},"end":{"date-parts":[[2025,4,11]]}},"container-title":["ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10887540\/10887541\/10890733.pdf?arnumber=10890733","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:27:04Z","timestamp":1774416424000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10890733\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,6]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/icassp49660.2025.10890733","relation":{},"subject":[],"published":{"date-parts":[[2025,4,6]]}}}