{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:17:59Z","timestamp":1774120679118,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T00:00:00Z","timestamp":1578528000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T00:00:00Z","timestamp":1578528000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701007"],"award-info":[{"award-number":["71701007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71531001"],"award-info":[{"award-number":["71531001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["71725002"],"award-info":[{"award-number":["71725002"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2018M640045"],"award-info":[{"award-number":["2018M640045"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s10115-019-01433-3","type":"journal-article","created":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T11:03:38Z","timestamp":1578567818000},"page":"2685-2708","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Fraud detection via behavioral sequence embedding"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4532-7109","authenticated-orcid":false,"given":"Guannan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren-yong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,9]]},"reference":[{"key":"1433_CR1","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: SIGKDD. ACM, New York, NY, USA, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"1433_CR2","unstructured":"Phua C, Lee V, Smith K, Gayler R (2010) A comprehensive survey of data mining-based fraud detection research. CoRR, vol. abs\/1009.6119"},{"key":"1433_CR3","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jnca.2016.04.007","volume":"68","author":"A Abdallah","year":"2016","unstructured":"Abdallah A, Maarof MA, Zainal A (2016) Fraud detection system: a survey. J Netw Comput Appl 68:90\u2013113","journal-title":"J Netw Comput Appl"},{"key":"1433_CR4","doi-asserted-by":"crossref","unstructured":"Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, pp 427\u2013438","DOI":"10.1145\/342009.335437"},{"issue":"3","key":"1433_CR5","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1023\/B:DAMI.0000023676.72185.7c","volume":"8","author":"K Yamanishi","year":"2004","unstructured":"Yamanishi K, Takeuchi JI, Williams G, Milne P (2004) On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Min Knowl Discov 8(3):275\u2013300","journal-title":"Data Min Knowl Discov"},{"issue":"4","key":"1433_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3056563","volume":"11","author":"B Hooi","year":"2017","unstructured":"Hooi B, Shin K, Song HA, Beutel A, Shah N, Faloutsos C (2017) Graph-based fraud detection in the face of camouflage. ACM Trans Knowl Discov Data 11(4):1\u201326","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1433_CR7","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.eswa.2017.08.043","volume":"91","author":"WN Robinson","year":"2018","unstructured":"Robinson WN, Aria A (2018) Sequential fraud detection for prepaid cards using hidden markov model divergence. Expert Syst Appl 91:235\u2013251","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1433_CR8","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.dss.2010.08.008","volume":"50","author":"S Bhattacharyya","year":"2011","unstructured":"Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50(3):602\u2013613","journal-title":"Decis Support Syst"},{"issue":"6","key":"1433_CR9","doi-asserted-by":"publisher","first-page":"3027","DOI":"10.1016\/j.eswa.2013.10.033","volume":"41","author":"S Tsang","year":"2014","unstructured":"Tsang S, Koh YS, Dobbie G, Alam S (2014) Detecting online auction shilling frauds using supervised learning. Expert Syst Appl 41(6):3027\u20133040","journal-title":"Expert Syst Appl"},{"key":"1433_CR10","unstructured":"Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. CoRR, vol. abs\/1506.00019"},{"key":"1433_CR11","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. CoRR, vol. abs\/1409.3215"},{"key":"1433_CR12","doi-asserted-by":"crossref","unstructured":"Li X, Zhao B, Lu X (2017) Mam-rnn: multi-level attention model based rnn for video captioning. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 2208\u20132214","DOI":"10.24963\/ijcai.2017\/307"},{"key":"1433_CR13","doi-asserted-by":"crossref","unstructured":"Zhai S, Chang Kh, Zhang R, Zhang ZM (2016) Deepintent: learning attentions for online advertising with recurrent neural networks. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1295\u20131304","DOI":"10.1145\/2939672.2939759"},{"key":"1433_CR14","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. CoRR, vol. abs\/1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"1433_CR15","unstructured":"Collins J, Sohl-Dickstein J, Sussillo D (2016) Capacity and trainability in recurrent neural networks. CoRR, vol. abs\/1611.09913"},{"key":"1433_CR16","unstructured":"Neil D, Pfeiffer M, Liu SC (2016) Phased lstm: Accelerating recurrent network training for long or event-based sequences. CoRR, vol. abs\/1610.09513"},{"key":"1433_CR17","doi-asserted-by":"crossref","unstructured":"Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, pp 194\u2013200","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"1433_CR18","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. CoRR, vol. abs\/1409.0473"},{"key":"1433_CR19","unstructured":"Chorowski J, Bahdanau D, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. In: Proceedings of the 28th international conference on neural information processing systems, pp 577\u2013585"},{"key":"1433_CR20","doi-asserted-by":"crossref","unstructured":"Chen J, Zhang H, He X, Nie L, Liu W, Chua TS (2017) Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 335\u2013344","DOI":"10.1145\/3077136.3080797"},{"key":"1433_CR21","doi-asserted-by":"crossref","unstructured":"Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) Deepmove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 world wide web conference, pp 1459\u20131468","DOI":"10.1145\/3178876.3186058"},{"key":"1433_CR22","doi-asserted-by":"crossref","unstructured":"Wang Y, Shen H, Liu S, Gao J, Cheng X (2017) Cascade dynamics modeling with attention-based recurrent neural network. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 2985\u20132991","DOI":"10.24963\/ijcai.2017\/416"},{"key":"1433_CR23","unstructured":"Cai H, Zheng VW, Chang KC (2017) A comprehensive survey of graph embedding: problems, techniques and applications. CoRR, vol. abs\/1709.07604"},{"key":"1433_CR24","doi-asserted-by":"publisher","DOI":"10.4135\/9781412985130","volume-title":"Multidimensional scaling","author":"JB Kruskal","year":"1978","unstructured":"Kruskal JB, Wish M (1978) Multidimensional scaling. CRC Press, Boca Raton"},{"issue":"5500","key":"1433_CR25","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"5500","key":"1433_CR26","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum JB, Silva Vd, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319\u20132323","journal-title":"Science"},{"key":"1433_CR27","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS. MIT Press, Cambridge, MA, USA, pp 585\u2013591"},{"key":"1433_CR28","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. CoRR, vol. abs\/1301.3781"},{"key":"1433_CR29","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: SIGKDD. ACM, New York, NY, USA, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"1433_CR30","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: WWW. Republic and canton of Geneva, Switzerland: international world wide web conferences steering committee, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"1433_CR31","doi-asserted-by":"crossref","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: SIGKDD. ACM, New York, NY, USA, pp 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"issue":"8","key":"1433_CR32","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":"1433_CR33","doi-asserted-by":"crossref","unstructured":"Guo J, Liu G, Zuo Y, Wu J (Nov 2018) Learning sequential behavior representations for fraud detection. In: 2018 IEEE international conference on data mining (ICDM), pp 127\u2013136","DOI":"10.1109\/ICDM.2018.00028"},{"key":"1433_CR34","doi-asserted-by":"crossref","unstructured":"Ma F, Chitta R, Zhou J, You Q, Sun T, Gao J (2017) Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1903\u20131911","DOI":"10.1145\/3097983.3098088"},{"key":"1433_CR35","unstructured":"Alterovitz G, Ramoni MF (2006) Discovering biological guilds through topological abstraction. In: AMIA annual symposium proceedings, vol 2006, p\u00a01. American medical informatics association"},{"key":"1433_CR36","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR, vol. abs\/1412.6980"},{"issue":"1","key":"1433_CR37","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"P-T de Boer","year":"2005","unstructured":"de Boer P-T, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19\u201367","journal-title":"Ann Oper Res"},{"issue":"9","key":"1433_CR38","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"1433_CR39","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TKDE.2005.95","volume":"17","author":"G Wu","year":"2005","unstructured":"Wu G, Chang EY (2005) Kba: kernel boundary alignment considering imbalanced data distribution. IEEE Trans Knowl Data Eng 17(6):786\u2013795","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-019-01433-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-019-01433-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-019-01433-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T16:40:47Z","timestamp":1665420047000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-019-01433-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,9]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["1433"],"URL":"https:\/\/doi.org\/10.1007\/s10115-019-01433-3","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,9]]},"assertion":[{"value":"12 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}