{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T04:17:27Z","timestamp":1783138647676,"version":"3.54.6"},"reference-count":18,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":99,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.04.166","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:09:06Z","timestamp":1780402146000},"page":"1247-1253","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders"],"prefix":"10.1016","volume":"280","author":[{"given":"Francesco","family":"Zola","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucia","family":"Mu\u00f1oz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Venturi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amaia","family":"Gil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.04.166_bib1","unstructured":"Europol, Iocta 2024, Internet Organised Crime Threat Assessment Accessed on 10\/09\/2025 (2024). URL https:\/\/www.europol.europa.eu\/cms\/sites\/default\/files\/documents\/Internet%20Organised%20Crime%20Threat\\%20Assessment%20IOCTA%202024.pdf"},{"issue":"2","key":"10.1016\/j.procs.2026.04.166_bib2","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1108\/JMLC-07-2024-0113","article-title":"Regulatory strategies for combatting money laundering in the era of digital trade","volume":"28","author":"Khan","year":"2025","journal-title":"Journal of Money Laundering Control"},{"issue":"5","key":"10.1016\/j.procs.2026.04.166_bib3","first-page":"47","article-title":"Raise the red flag: a recent study examines which sas no","volume":"62","author":"Moyes","year":"2005","journal-title":"99 indicators are more effective in detecting fraudulent financial reporting, Internal Auditor"},{"key":"10.1016\/j.procs.2026.04.166_bib4","unstructured":"Europol, Eu-socta 2025, Serious and Organised Crime Threat Assessment Accessed on 10\/09\/2025 (2025). URL https:\/\/www.europol.europa.eu\/cms\/sites\/default\/files\/documents\/EU-SOCTA-2025.pdf"},{"key":"10.1016\/j.procs.2026.04.166_bib5","first-page":"302005","article-title":"Complex networks-based anomaly detection for financial transactions in anti-money laundering","volume":"55","author":"Oliveira","year":"2025","journal-title":"Forensic Science International: Digital Investigation"},{"key":"10.1016\/j.procs.2026.04.166_bib6","doi-asserted-by":"crossref","first-page":"47699","DOI":"10.1109\/ACCESS.2022.3170467","article-title":"Anomaly detection in graphs of bank transactions for anti money laundering applications","volume":"10","author":"Dumitrescu","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.procs.2026.04.166_bib7","doi-asserted-by":"crossref","first-page":"29851","DOI":"10.52202\/075280-1300","article-title":"Realistic synthetic financial transactions for anti-money laundering models","volume":"36","author":"Altman","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.procs.2026.04.166_bib8","doi-asserted-by":"crossref","unstructured":"F. Zola, J. Medina, A. Venturi, R. Orduna, A graph machine learning approach for detecting topological patterns in transactional graphs, arXiv preprint arXiv:2509.12730 (2025).","DOI":"10.1109\/ICDMW69685.2025.00025"},{"key":"10.1016\/j.procs.2026.04.166_bib9","unstructured":"T. Kipf, Semi-supervised Classification with graph convolutional networks, arXiv preprint arXiv:1609.02907 (2016)."},{"key":"10.1016\/j.procs.2026.04.166_bib10","unstructured":"W. Hamilton, Z. Ying, J. Leskovec, Inductive representation learning on large graphs, Advances in neural information processing systems (2017)."},{"key":"10.1016\/j.procs.2026.04.166_bib11","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, arXiv preprint arXiv:1710.10903 (2017)."},{"key":"10.1016\/j.procs.2026.04.166_bib12","series-title":"Unveiling dynamics and patterns: A comprehensive analysis of spreading patterns and similarities in low-labelled ransomware families, in: 2024 IEEE International Conference on Blockchain (Blockchain)","first-page":"260","author":"Zola","year":"2024"},{"key":"10.1016\/j.procs.2026.04.166_bib13","unstructured":"I. Research, Amlsim: Anti-money laundering simulator, https:\/\/github.com\/IBM\/AMLSim (2018)."},{"key":"10.1016\/j.procs.2026.04.166_bib14","unstructured":"A. Coalition, Ai4finance open datasets for financial crime research, https:\/\/sites.google.com\/view\/ai4fcf\/open-datasets (2023)."},{"key":"10.1016\/j.procs.2026.04.166_bib15","series-title":"Paysim: A financial mobile money simulator for fraud detection, in: 28th European Modeling and Simulation Symposium, EMSS, Larnaca","first-page":"249","author":"Lopez-Rojas","year":"2016"},{"key":"10.1016\/j.procs.2026.04.166_bib16","doi-asserted-by":"crossref","first-page":"41720","DOI":"10.1109\/ACCESS.2022.3167699","article-title":"Amaretto: An active learning framework for money laundering detection","volume":"10","author":"Labanca","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.procs.2026.04.166_bib17","series-title":"Enhancing anti-money laundering: Development of a synthetic transaction monitoring dataset, in: 2023 IEEE International Conference on e-Business Engineering (ICEBE)","first-page":"47","author":"Oztas","year":"2023"},{"issue":"1","key":"10.1016\/j.procs.2026.04.166_bib18","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE transactions on neural networks and learning systems"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926011798?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926011798?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T03:34:10Z","timestamp":1783136050000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926011798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":18,"alternative-id":["S1877050926011798"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.04.166","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.04.166","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}