{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:32:26Z","timestamp":1773246746540,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031746420","type":"print"},{"value":"9783031746437","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-74643-7_29","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T23:20:54Z","timestamp":1735687254000},"page":"402-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Topology-Agnostic Detection of\u00a0Temporal Money Laundering Flows in\u00a0Billion-Scale Transactions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-3714","authenticated-orcid":false,"given":"Haseeb","family":"Tariq","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4027-4351","authenticated-orcid":false,"given":"Marwan","family":"Hassani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"29_CR1","unstructured":"Welling, S.N.: Smurfs, money laundering and the federal criminal law: the crime of structuring transactions. In: Fla. Law 41, pp. 287\u2013343 (1989). https:\/\/uknowledge.uky.edu\/law_facpub\/344\/"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Dunbar, R.I.M.: Coevolution of neocortical size, group size and language in humans. Behav. Brain Sci. 16(4), 681\u2013694 (1993). https:\/\/doi.org\/10.1017\/S0140525X00032325","DOI":"10.1017\/S0140525X00032325"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577\u20138582 (2006). https:\/\/doi.org\/10.1073\/pnas.0601602103, https:\/\/www.pnas.org\/doi\/pdf\/10.1073\/pnas.0601602103","DOI":"10.1073\/pnas.0601602103"},{"key":"29_CR4","doi-asserted-by":"publisher","unstructured":"Blondel, V.D., et al.: Fast unfolding of communities in large networks. J. Stat. Mech. Theo. Exp. 2008(10), P10008 (2008). https:\/\/doi.org\/10.1088\/1742-5468\/2008\/10\/p10008","DOI":"10.1088\/1742-5468\/2008\/10\/p10008"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3). ISSN: 0360-0300 (2009). https:\/\/doi.org\/10.1145\/1541880.1541882","DOI":"10.1145\/1541880.1541882"},{"key":"29_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-642-13672-6_40","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"L Akoglu","year":"2010","unstructured":"Akoglu, L., McGlohon, M., Faloutsos, C.: oddball: spotting anomalies in weighted graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6119, pp. 410\u2013421. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13672-6_40"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Shvachko, K., et al.: The Hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1\u201310 (2010). https:\/\/doi.org\/10.1109\/MSST.2010.5496972","DOI":"10.1109\/MSST.2010.5496972"},{"key":"29_CR8","unstructured":"Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. NSDI 2012, San Jose, CA. USENIX Association, p. 2 (2012)"},{"key":"29_CR9","doi-asserted-by":"publisher","unstructured":"De Domenico, M., et al.: Mathematical formulation of multilayer networks. Phys. Rev. X 3, 041022 (2013). https:\/\/doi.org\/10.1103\/PhysRevX.3.041022, https:\/\/link.aps.org\/doi\/10.1103\/PhysRevX.3.041022","DOI":"10.1103\/PhysRevX.3.041022"},{"key":"29_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-319-14325-5_12","volume-title":"Euro-Par 2014: Parallel Processing Workshops","author":"E Carlini","year":"2014","unstructured":"Carlini, E., Dazzi, P., Esposito, A., Lulli, A., Ricci, L.: Balanced graph partitioning with Apache spark. In: Lopes, L., et al. (eds.) Euro-Par 2014. LNCS, vol. 8805, pp. 129\u2013140. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-14325-5_12"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Wu, H., et al.: Path problems in temporal graphs. Proc. VLDB Endow. 7(9), 721\u2013732 (2014). ISSN: 2150-8097. https:\/\/doi.org\/10.14778\/2732939.2732945","DOI":"10.14778\/2732939.2732945"},{"issue":"3","key":"29_CR12","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2014","unstructured":"Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626\u2013688 (2014). https:\/\/doi.org\/10.1007\/s10618-014-0365-y","journal-title":"Data Min. Knowl. Disc."},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Huang, S., Fu, A., Liu, R.: Minimum spanning trees in temporal graphs, 419\u2013430 (2015). https:\/\/doi.org\/10.1145\/2723372.2723717","DOI":"10.1145\/2723372.2723717"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Dave, A., et al.: GraphFrames: an integrated API for mixing graph and relational queries, 1\u20138 (2016). https:\/\/doi.org\/10.1145\/2960414.2960416","DOI":"10.1145\/2960414.2960416"},{"key":"29_CR15","unstructured":"Savage, D., et al.: Detection of money laundering groups using supervised learning in networks (2016)"},{"key":"29_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2016.2594065","volume":"28","author":"W Huanhuan","year":"2016","unstructured":"Huanhuan, W., et al.: Efficient algorithms for temporal path computation. IEEE Trans. Knowl. Data Eng. 28, 1\u20131 (2016). https:\/\/doi.org\/10.1109\/TKDE.2016.2594065","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Paranjape, A., Benson, A.R., Leskovec, J.: Motifs in temporal networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM (2017). https:\/\/doi.org\/10.1145\/3018661.3018731","DOI":"10.1145\/3018661.3018731"},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Scholtes, I.: When is a Network a Network? In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2017). https:\/\/doi.org\/10.1145\/3097983.3098145","DOI":"10.1145\/3097983.3098145"},{"key":"29_CR19","doi-asserted-by":"publisher","unstructured":"Sharma, R., Oliveira, S.: Community detection algorithm for big social networks using hybrid architecture. Big Data Res. 10, 44\u201352 (2017). ISSN: 2214-5796. https:\/\/doi.org\/10.1016\/j.bdr.2017.10.003. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214579616302349","DOI":"10.1016\/j.bdr.2017.10.003"},{"key":"29_CR20","doi-asserted-by":"publisher","unstructured":"Qiu, X., et al.: Real-time constrained cycle detection in large dynamic graphs. Proc. VLDB Endow. 11(12), 1876\u2013 1888 (2018). ISSN: 2150-8097. https:\/\/doi.org\/10.14778\/3229863.3229874","DOI":"10.14778\/3229863.3229874"},{"key":"29_CR21","doi-asserted-by":"publisher","unstructured":"Wagenseller, P., Wang, F., Weili, W.: Size Matters: A Comparative Analysis of Community Detection Algorithms. 2018, 2875626 (2018). https:\/\/doi.org\/10.1109\/TCSS","DOI":"10.1109\/TCSS"},{"key":"29_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/JIFS-17682","volume":"35","author":"Z Zhao","year":"2018","unstructured":"Zhao, Z., et al.: A comparative study on community detection methods in complex networks. J. Intell. Fuzzy Syst. 35, 1\u201310 (2018). https:\/\/doi.org\/10.3233\/JIFS-17682","journal-title":"J. Intell. Fuzzy Syst."},{"key":"29_CR23","unstructured":"Elliott, A., et al.: Anomaly detection in networks with application to financial transaction networks (2019). arXiv: 1901.00402 [stat.AP]"},{"key":"29_CR24","doi-asserted-by":"publisher","unstructured":"Traag, V.A., Waltman, L., van Eck, N.J.: From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9(1), 5233 (2019). ISSN: 2045-2322. https:\/\/doi.org\/10.1038\/s41598-019-41695-z","DOI":"10.1038\/s41598-019-41695-z"},{"issue":"4","key":"29_CR25","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/s41019-019-00105-0","volume":"4","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Yuan, Y., Ma, Y., Wang, G.: Time-dependent graphs: definitions, applications, and algorithms. Data Sci. Eng. 4(4), 352\u2013366 (2019). https:\/\/doi.org\/10.1007\/s41019-019-00105-0","journal-title":"Data Sci. Eng."},{"key":"29_CR26","doi-asserted-by":"publisher","unstructured":"Li, X., et al.: FlowScope: spotting money laundering based on graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 4731\u20134738 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.5906. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5906","DOI":"10.1609\/aaai.v34i04.5906"},{"key":"29_CR27","doi-asserted-by":"publisher","unstructured":"Ma, X., et al.: A comprehensive survey on graph anomaly detection with deep learning. IEEE Trans. Knowl. Data Eng. 1 (2021). https:\/\/doi.org\/10.1109\/TKDE.2021.3118815","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"29_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-030-86514-6_11","volume-title":"Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track","author":"M Starnini","year":"2021","unstructured":"Starnini, M., et al.: Smurf-based anti-money laundering in time-evolving transaction networks. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12978, pp. 171\u2013186. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86514-6_11"},{"key":"29_CR29","doi-asserted-by":"publisher","unstructured":"Dumitrescu, B., B\u0103ltoiu, A., Budulan, \u015e.: Anomaly detection in graphs of bank transactions for anti money laundering applications. IEEE Access 10, 47699\u201347714 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3170467","DOI":"10.1109\/ACCESS.2022.3170467"},{"key":"29_CR30","doi-asserted-by":"publisher","unstructured":"Granados, O., Vargas, A.: The geometry of suspicious money laundering activities in financial networks. EPJ Data Sci. 11 (2022). https:\/\/doi.org\/10.1140\/epjds\/s13688-022-00318-w","DOI":"10.1140\/epjds\/s13688-022-00318-w"},{"key":"29_CR31","doi-asserted-by":"publisher","unstructured":"Peng, Y., et al.: TDB: breaking all hop-constrained cycles in billion- scale directed graphs (2023). https:\/\/doi.org\/10.1109\/ICDE55515.2023.00018.","DOI":"10.1109\/ICDE55515.2023.00018."},{"key":"29_CR32","unstructured":"Financial Action Task Force (FATF). FATF Black and Grey Lists. https:\/\/www.fatf-gafi.org\/en\/countries\/black-and-grey-lists.html. Accessed 25 Mar 2023"},{"key":"29_CR33","unstructured":"NVB. Transaction Monitoring Netherlands: a unique step in the fight against money laundering and the financing of terrorism. https:\/\/www.nvb.nl\/english\/transaction-monitoring-netherlands-aunique-step-in-the-fight-against-money-laundering-and-thefinancing-of-terrorism\/. Accessed 14 June 2023"},{"key":"29_CR34","unstructured":"BIIA. Singapore Banks To Share Information Voluntarily To Fight Money Laundering. https:\/\/www.biia.com\/singapore-banks-to-shareinformation-voluntarily-to-fight-money-laundering\/. Accessed 14 June 2023"},{"key":"29_CR35","unstructured":"BIS. BIS concludes Project Aurora, a proof of concept based on the use of data, technology and collaboration to combat money laundering across institutions and borders. https:\/\/www.bis.org\/about\/bisih\/topics\/fmis\/aurora.htm. Accessed 14 June 2023"},{"key":"29_CR36","unstructured":"The United Nations Office on Drugs and Crime (UNODC). Money Laundering Overview. https:\/\/www.unodc.org\/unodc\/en\/money-laundering\/overview.html. Accessed 25 Mar 2023"},{"key":"29_CR37","unstructured":"Europol. Money Muling. https:\/\/www.europol.europa.eu\/operations-services-and-innovation\/public-awareness-and-prevention-guides\/money-muling. Accessed 14 June 2023"},{"key":"29_CR38","unstructured":"Actu IA. The ACPR launches an experiment on data sharing to combat money laundering and terrorist financing. https:\/\/www.actuia.com\/english\/the-acpr-launches-an-experiment-on-data-sharingto- combat-money-laundering-and-terrorist-financing\/. Accessed 14 June 2023"},{"key":"29_CR39","unstructured":"iban.org. International Bank Account Number. https:\/\/www.iban.org\/. Accessed 25 Mar 2023"},{"key":"29_CR40","unstructured":"Moody\u2019s. UBOs: what they are, disclosure requirements, and the data challenge. https:\/\/www.moodys.com\/web\/en\/us\/kyc\/resources\/insights\/ubos-what-they-are-disclosure-requirements-data-challenge.html. Accessed 22 June 2023"},{"key":"29_CR41","unstructured":"UnixTime.org. Unix Timestamp. https:\/\/unixtime.org\/. Accessed 21 June 2023"},{"key":"29_CR42","unstructured":"Society for Worldwide Interbank Financial Telecommunications (SWIFT). What is an Ultimate Beneficial Owner. https:\/\/www.swift.com\/your-needs\/financial-crime-cyber-security\/know-your-customer-kyc\/ultimate-beneficial-owner-ubo. Accessed 25 Mar 2023"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74643-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T02:37:05Z","timestamp":1735699025000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74643-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746420","9783031746437"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74643-7_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}