{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:47:30Z","timestamp":1784238450852,"version":"3.55.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2022YFB3305802"],"award-info":[{"award-number":["2022YFB3305802"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Soc. Syst."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/tcss.2025.3589343","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T18:08:00Z","timestamp":1754417280000},"page":"584-600","source":"Crossref","is-referenced-by-count":1,"title":["DyHom-SSL: Homophily-Aware Semisupervised Learning With Dynamic Pseudolabels for Bitcoin Money Laundering Detection"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8804-7150","authenticated-orcid":false,"given":"Jing","family":"Huang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4662-1721","authenticated-orcid":false,"given":"Jiahui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-4075","authenticated-orcid":false,"given":"Honggui","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","author":"Nakamoto","year":"2008","journal-title":"Bitcoin: A Peer-to-Peer Electronic Cash System."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1049\/icp.2022.0387"},{"key":"ref3","first-page":"4","article-title":"Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics","volume-title":"Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining","author":"Weber","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599803"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3391195"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0086197"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.52"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-4139-7_10"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.5220\/0004937303690374"},{"key":"ref10","article-title":"Anomaly detection in the bitcoin system: A network perspective","author":"Pham"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2504730.2504747"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3231687"},{"key":"ref13","article-title":"Anomaly detection in bitcoin network using unsupervised learning methods","author":"Pham"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/nana.2019.00083"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3533271.3561727"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422549"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00558-z"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24251\/HICSS.2022.194"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1108\/JMLC-09-2021-0106"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1144-z"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3049278"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3213335"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2023.102055"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/e26030211"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467142"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122156"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.23919\/APNOMS50412.2020.9236780"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111698"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/electronics14081648"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/app13158766"},{"key":"ref31","article-title":"Consistency augment training with learnable data augmentation for graph anomaly detection with limited supervision","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Chen","year":"2024"},{"key":"ref32","article-title":"SemiReward: A general reward model for semi-supervised learning","author":"Li","year":"2023"},{"key":"ref33","first-page":"6448","article-title":"Does label smoothing mitigate label noise?","volume-title":"Proc. Int. Conf. Mach. Learn.,","author":"Lukasik","year":"2020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20307"},{"key":"ref35","first-page":"11828","article-title":"Smoothed adaptive weighting for imbalanced semi-supervised learning: Improve reliability against unknown distribution data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lai","year":"2022"},{"key":"ref36","first-page":"24","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations (ICLR)","author":"Kipf","year":"2017"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"ref38","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref39","article-title":"How attentive are graph attention networks?","author":"Brody"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2021.3062944"},{"key":"ref41","first-page":"36","article-title":"Deep insights into noisy pseudo labeling on graph data","volume-title":"Proc. Adv. Neur. Inf. Process. Syst.","author":"Botao","year":"2024"},{"key":"ref42","first-page":"21076","article-title":"Rethinking graph neural networks for anomaly detection","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML),","author":"Tang","year":"2022"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_46"},{"key":"ref46","first-page":"29851","article-title":"Realistic synthetic financial transactions for anti-money laundering models","volume":"36","author":"Altman","year":"2023","journal-title":"Adv. Neur. Inf. Process. Syst."}],"container-title":["IEEE Transactions on Computational Social Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6570650\/11395558\/11114922.pdf?arnumber=11114922","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:41:34Z","timestamp":1771047694000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11114922\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":46,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tcss.2025.3589343","relation":{},"ISSN":["2329-924X","2373-7476"],"issn-type":[{"value":"2329-924X","type":"electronic"},{"value":"2373-7476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}