{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:03:14Z","timestamp":1778947394536,"version":"3.51.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Elliptic data\u2014one of the largest Bitcoin transaction graphs\u2014has admitted promising results in many studies using classical supervised learning and graph convolutional network models for anti-money laundering. Despite the promising results provided by these studies, only few have considered the temporal information of this dataset, wherein the results were not very satisfactory. Moreover, there is very sparse existing literature that applies active learning to this type of blockchain dataset. In this paper, we develop a classification model that combines long-short-term memory with GCN\u2014referred to as temporal-GCN\u2014that classifies the illicit transactions of Elliptic data using its transaction\u2019s features only. Subsequently, we present an active learning framework applied to the large-scale Bitcoin transaction graph dataset, unlike previous studies on this dataset. Uncertainties for active learning are obtained using Monte-Carlo dropout (MC-dropout) and Monte-Carlo based adversarial attack (MC-AA) which are Bayesian approximations. Active learning frameworks with these methods are compared using various acquisition functions that appeared in the literature. To the best of our knowledge, MC-AA method is the first time to be examined in the context of active learning. Our main finding is that temporal-GCN model has attained significant success in comparison to the previous studies with the same experimental settings on the same dataset. Moreover, we evaluate the performance of the provided acquisition functions using MC-AA and MC-dropout and compare the result against the baseline random sampling model.<\/jats:p>","DOI":"10.1007\/s11063-022-10904-8","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T03:39:21Z","timestamp":1655350761000},"page":"689-707","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Graph-Based LSTM for Anti-money Laundering: Experimenting Temporal Graph Convolutional Network with Bitcoin Data"],"prefix":"10.1007","volume":"55","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":[[2022,6,16]]},"reference":[{"key":"10904_CR1","unstructured":"Ciphertrace (2020) spring 2020 cryptocurrency crime and anti-money laundering report. https:\/\/ciphertrace.com\/spring-2020-cryptocurrency-anti-money-laundering-report\/. Accessed 2021-09-01"},{"key":"10904_CR2","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"},{"key":"10904_CR3","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. arXiv preprint arXiv:1908.02591"},{"key":"10904_CR4","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":"10904_CR5","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":"10904_CR6","doi-asserted-by":"crossref","unstructured":"Pareja A, Domeniconi G, Chen J, Ma J, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) Evolvegcn: evolving graph convolu- tional networks for dynamic graphs. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 5363\u20135370","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"10904_CR7","doi-asserted-by":"crossref","unstructured":"Poursafaei F, Rabbany R, Zilic Z (20201) Sigtran: signature vectors for detecting illicit activities in blockchain transaction networks. In: PAKDD (1). Springer, pp 27\u201339","DOI":"10.1007\/978-3-030-75762-5_3"},{"issue":"2","key":"10904_CR8","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 mc-dropout in general dataset: uncertainty estimation in bitcoin. Neural Process Lett 53(2):1001\u20131011","journal-title":"Neural Process Lett"},{"key":"10904_CR9","doi-asserted-by":"crossref","unstructured":"Lorenz J, Silva MI, Apar\u00b4 \u0131cio D, Ascens\u02dcao JT, Bizarro P (2020) Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. arXiv preprint arXiv:2005.14635","DOI":"10.1145\/3383455.3422549"},{"key":"10904_CR10","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: represent ing model uncertainty in deep learning. In: international conference on machine learning. PMLR, pp 1050\u20131059"},{"key":"10904_CR11","doi-asserted-by":"crossref","unstructured":"Alarab I, Prakoonwit S (2021) Adversarial attack for uncertainty estimation: identifying critical regions in neural networks. arXiv:2107.07618","DOI":"10.1007\/s11063-021-10707-3"},{"key":"10904_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113318","volume":"150","author":"S Farrugia","year":"2020","unstructured":"Farrugia S, Ellul J, Azzopardi G (2020) Detection of illicit accounts over the ethereum blockchain. Expert Syst Appl 150:113318","journal-title":"Expert Syst Appl"},{"key":"10904_CR13","unstructured":"Settles B (2009) Active learning literature survey"},{"issue":"1","key":"10904_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13634-015-0293-z","volume":"2016","author":"J Qiu","year":"2016","unstructured":"Qiu J, Wu Q, Ding G, Xu Y, Feng S (2016) A survey of machine learning for big data processing. EURASIP J Adv Signal Process 2016(1):1\u201316","journal-title":"EURASIP J Adv Signal Process"},{"key":"10904_CR15","doi-asserted-by":"crossref","unstructured":"Lewis DD, Catlett J (1994) Heterogeneous uncertainty sampling for supervised learning. In: Machine learning proceedings 1994. Elsevier, pp 148\u2013156","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"10904_CR16","unstructured":"Settles B, Craven M, Ray S (2007) Multiple-instance active learning. In: Advances in neural information processing systems, vol 20"},{"key":"10904_CR17","unstructured":"Gal Y, Islam R, Ghahramani Z (2017) Deep Bayesian active learning with image data. In: International conference on machine learning. PMLR, pp 1183\u20131192"},{"key":"10904_CR18","unstructured":"Gal Y, Hron J, Kendall A (2017) Concrete dropout. arXiv preprint arXiv:1705.07832"},{"issue":"3","key":"10904_CR19","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379\u2013423","journal-title":"Bell Syst Tech J"},{"key":"10904_CR20","unstructured":"Houlsby N, Husz\u00b4ar F, Ghahramani Z, Lengyel M (2011) Bayesian active learning for classification and preference learning. arXiv preprint arXiv:1112.5745"},{"issue":"3","key":"10904_CR21","first-page":"455","volume":"44","author":"EH Johnson","year":"1966","unstructured":"Johnson EH (1966) Freeman: elementary applied statistics: for students in behavioral science (book review). Soc Forces 44(3):455","journal-title":"Soc Forces"},{"key":"10904_CR22","unstructured":"Kendall A, Badrinarayanan V, Cipolla R (2015) Bayesian segnet: model uncer- tainty in deep convolutional encoder-decoder architectures for scene under- standing. arXiv preprint arXiv:1511.02680"},{"key":"10904_CR23","doi-asserted-by":"crossref","unstructured":"Kampffmeyer M, Salberg A-B, Jenssen R (2016) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1\u20139","DOI":"10.1109\/CVPRW.2016.90"},{"issue":"3","key":"10904_CR24","first-page":"4","volume":"1","author":"Y Gal","year":"2016","unstructured":"Gal Y (2016) Uncertainty in deep learning. Univ Camb 1(3):4","journal-title":"Univ Camb"},{"issue":"1","key":"10904_CR25","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"10904_CR26","unstructured":"Chakraborty A, Alam M, Dey V, Chattopadhyay A, Mukhopadhyay D (2018) Adversarial attacks and defences: a survey. arXiv preprint arXiv:1810.00069"},{"key":"10904_CR27","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572"},{"key":"10904_CR28","unstructured":"Lewis DD, Gale WA (1994) A sequential algorithm for training text classifiers. CoRR abs\/cmp-lg\/9407020. arXiv:cmp-lg\/9407020. http:\/\/arxiv.org\/abs\/cmp-lg\/9407020"},{"key":"10904_CR29","first-page":"7026","volume":"32","author":"A Kirsch","year":"2019","unstructured":"Kirsch A, Van Amersfoort J, Gal Y (2019) Batchbald: Efficient and diverse batch acquisition for deep Bayesian active learning. Adv Neural Inf Process Syst 32:7026\u20137037","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"10904_CR30","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"10904_CR31","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recur- rent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"10904_CR32","unstructured":"Srivastava N, Mansimov E, Salakhudinov R (2015) Unsupervised learning of video representations using lstms. In: International conference on machine learning. PMLR, pp 843\u2013852"},{"key":"10904_CR33","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104\u20133112"},{"key":"10904_CR34","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850"},{"key":"10904_CR35","unstructured":"Du J, Zhang S, Wu G, Moura JM, Kar S (2017) Topology adaptive graph convolutional networks. arXiv preprint arXiv:1710.10370"},{"key":"10904_CR36","first-page":"3844","volume":"29","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844\u20133852","journal-title":"Adv Neural Inf Process Syst"},{"key":"10904_CR37","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolu tional networks. arXiv preprint arXiv:1609.02907"},{"key":"10904_CR38","doi-asserted-by":"crossref","unstructured":"Seo Y, Defferrard M, Vandergheynst P, Bresson X (2018) Structured sequence modeling with graph convolutional recurrent networks. In: International conference on neural information processing. Springer, pp 362\u2013373","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"10904_CR39","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428"},{"issue":"6","key":"10904_CR40","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80\u201383","journal-title":"Biom Bull"},{"key":"10904_CR41","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":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10904-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10904-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10904-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T14:27:53Z","timestamp":1678112873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10904-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10904"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10904-8","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,16]]},"assertion":[{"value":"25 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}