{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:31:19Z","timestamp":1774452679012,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit\/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and <jats:inline-formula><jats:alternatives><jats:tex-math>$$f_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>f<\/mml:mi>\n                    <mml:mn>1<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>-score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.<\/jats:p>","DOI":"10.1007\/s11042-023-17323-4","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T06:09:10Z","timestamp":1703225350000},"page":"58449-58464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrencies"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8320-6423","authenticated-orcid":false,"given":"Ismail","family":"Alarab","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simant","family":"Prakoonwit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"17323_CR1","unstructured":"Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Decentralized Bus Rev 21260"},{"key":"17323_CR2","unstructured":"Brenig C, Accorsi R, M\u00fcller G (2015) Economic analysis of cryptocurrency backed money laundering. ECIS 2015 Completed Research Papers 20"},{"key":"17323_CR3","doi-asserted-by":"publisher","first-page":"163965","DOI":"10.1109\/ACCESS.2021.3134076","volume":"9","author":"J Nicholls","year":"2021","unstructured":"Nicholls J, Kuppa A, Le-Khac N-A (2021) Financial cybercrime: a comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. IEEE Access 9:163965\u2013163986","journal-title":"IEEE Access"},{"key":"17323_CR4","doi-asserted-by":"crossref","unstructured":"Meiklejohn S, Pomarole M, Jordan G, Levchenko K, McCoy D, Voelker GM, Savage S (2013) A fistful of bitcoins: characterizing payments among men with no names. In: Proceedings of the 2013 conference on internet measurement conference, pp 127\u2013140","DOI":"10.1145\/2504730.2504747"},{"key":"17323_CR5","unstructured":"Weber M, Domeniconi G, Chen J, Weidele DKI, Bellei C, Robinson T, Leiserson CE (2019) Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics"},{"key":"17323_CR6","doi-asserted-by":"crossref","unstructured":"Alarab I, Prakoonwit S, Nacer MI (2020) Comparative analysis using supervised learning methods for anti-money laundering in bitcoin. In: Proceedings of the 2020 5th international conference on machine learning technologies, pp 11\u201317","DOI":"10.1145\/3409073.3409078"},{"key":"17323_CR7","doi-asserted-by":"crossref","unstructured":"Alarab I, Prakoonwit S, Nacer MI (2020) Competence of graph convolutional networks for anti-money laundering in bitcoin blockchain. In: Proceedings of the 2020 5th international conference on machine learning technologies, pp 23\u201327","DOI":"10.1145\/3409073.3409080"},{"key":"17323_CR8","doi-asserted-by":"publisher","first-page":"5363","DOI":"10.1609\/aaai.v34i04.5984","volume":"34","author":"A Pareja","year":"2020","unstructured":"Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) Evolvegcn: evolving graph convolutional networks for dynamic graphs. Proceedings of the AAAI conference on artificial intelligence 34:5363\u20135370","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"issue":"2","key":"17323_CR9","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1007\/s11063-021-10424-x","volume":"53","author":"I Alarab","year":"2021","unstructured":"Alarab I, Prakoonwit S, Nacer MI (2021) Illustrative discussion of mcdropout in general dataset: uncertainty estimation in bitcoin. Neural Process Lett 53(2):1001\u20131011","journal-title":"Neural Process Lett"},{"key":"17323_CR10","doi-asserted-by":"crossref","unstructured":"Alarab I, Prakoonwit S (2021) Adversarial attack for uncertainty estimation: identifying critical regions in neural networks. Neural Process Lett 1\u201317","DOI":"10.1007\/s11063-021-10707-3"},{"key":"17323_CR11","unstructured":"Oliveira C, Torres J, Silva MI, Apar\u00edcio D, Ascens\u00e3o JT, Bizarro P (2021) Guiltywalker: distance to illicit nodes in the bitcoin network. arXiv:2102.05373"},{"key":"17323_CR12","doi-asserted-by":"publisher","first-page":"37229","DOI":"10.1109\/ACCESS.2021.3062652","volume":"9","author":"XF Liu","year":"2021","unstructured":"Liu XF, Jiang X-J, Liu S-H, Tse CK (2021) Knowledge discovery in cryptocurrency transactions: a survey. IEEE Access 9:37229\u201337254","journal-title":"IEEE Access"},{"key":"17323_CR13","doi-asserted-by":"publisher","unstructured":"Reid F, Harrigan M (2013) An analysis of anonymity in the bitcoin system. In: Altshuler Y, Elovici Y, Cremers A, Aharony N, Pentland A (eds) Security and Privacy in Social Networks Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-1-4614-4139-7_10","DOI":"10.1007\/978-1-4614-4139-7_10"},{"key":"17323_CR14","doi-asserted-by":"crossref","unstructured":"Ron D, Shamir A (2013) Quantitative analysis of the full bitcoin transaction graph. In: International conference on financial cryptography and data security, Springer, pp 6\u201324","DOI":"10.1007\/978-3-642-39884-1_2"},{"key":"17323_CR15","doi-asserted-by":"crossref","unstructured":"Spagnuolo M, Maggi F, Zanero S (2014) Bitiodine: extracting intelligence from the bitcoin network. In: International conference on financial cryptography and data security, Springer, pp 457\u2013468","DOI":"10.1007\/978-3-662-45472-5_29"},{"issue":"2","key":"17323_CR16","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3390\/fi5020237","volume":"5","author":"M Ober","year":"2013","unstructured":"Ober M, Katzenbeisser S, Hamacher K (2013) Structure and anonymity of the bitcoin transaction graph. Futur Internet 5(2):237\u2013250","journal-title":"Futur Internet"},{"key":"17323_CR17","unstructured":"Baumann A, Fabian B, Lischke M (2014) Exploring the bitcoin network. In: WEBIST (1), pp 369\u2013374"},{"key":"17323_CR18","doi-asserted-by":"crossref","unstructured":"Di Battista G, Di Donato V, Patrignani M, Pizzonia M, Roselli V, Tamassia R (2015) Bitconeview: visualization of flows in the bitcoin transaction graph. In: 2015 IEEE Symposium on visualization for cyber security (VizSec), pp 1\u20138. IEEE","DOI":"10.1109\/VIZSEC.2015.7312773"},{"key":"17323_CR19","unstructured":"Pham T, Lee S (2016) Anomaly detection in the bitcoin system-a network perspective. arXiv:1611.03942"},{"key":"17323_CR20","doi-asserted-by":"crossref","unstructured":"Harlev MA, Sun Yin H, Langenheldt KC, Mukkamala R, Vatrapu R (2018) Breaking bad: de-anonymising entity types on the bitcoin blockchain using supervised machine learning. In: Proceedings of the 51st Hawaii international conference on system sciences","DOI":"10.24251\/HICSS.2018.443"},{"issue":"3","key":"17323_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00558-z","volume":"2","author":"D Vassallo","year":"2021","unstructured":"Vassallo D, Vella V, Ellul J (2021) Application of gradient boosting algorithms for anti-money laundering in cryptocurrencies. SN Comput Sci 2(3):1\u201315","journal-title":"SN Comput Sci"},{"key":"17323_CR22","doi-asserted-by":"crossref","unstructured":"Eloul S, Moran SJ, Mendel J (2021) Improving streaming cryptocurrency transaction classification via biased sampling and graph feedback. In: Annual computer security applications conference, pp 761\u2013772","DOI":"10.1145\/3485832.3485913"},{"issue":"3","key":"17323_CR23","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1108\/JMLC-07-2021-0076","volume":"25","author":"G-Y Sheu","year":"2022","unstructured":"Sheu G-Y, Li C-Y (2022) On the potential of a graph attention network in money laundering detection. J Money Laund Control 25(3):594\u2013608. https:\/\/doi.org\/10.1108\/JMLC-07-2021-0076","journal-title":"J Money Laund Control"},{"key":"17323_CR24","doi-asserted-by":"crossref","unstructured":"Xia P, Ni Z, Xiao H, Zhu X, Peng P (2021) A novel spatiotemporal prediction approach based on graph convolution neural networks and long short-term memory for money laundering fraud. Arab J Sci Eng 1\u201317","DOI":"10.1007\/s13369-021-06116-2"},{"key":"17323_CR25","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed A-r, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 6645\u20136649. Ieee","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"17323_CR26","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"17323_CR27","doi-asserted-by":"publisher","first-page":"4602","DOI":"10.1609\/aaai.v33i01.33014602","volume":"33","author":"C Morris","year":"2019","unstructured":"Morris C, Ritzert M, Fey M, Hamilton WL, Lenssen JE, Rattan G, Grohe M (2019) Weisfeiler and leman go neural: higher-order graph neural networks. Proceedings of the AAAI conference on artificial intelligence 33:4602\u20134609","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"17323_CR28","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"key":"17323_CR29","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv:1810.00826"},{"key":"17323_CR30","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with pytorch geometric. arXiv:1903.02428"},{"key":"17323_CR31","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17323-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17323-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17323-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T09:25:35Z","timestamp":1717406735000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17323-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":31,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["17323"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17323-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,22]]},"assertion":[{"value":"3 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Yes","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Yes","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that there are no conflicts of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}