1 |
郝光昊.数字化欺诈与金融科技反欺诈的应用[J].税务与经济,2019(6):40-47. HAO G H. The application of digital fraud and financial technology anti-fraud[J]. Taxation and Economics, 2019(6): 40-47.
|
2 |
XU J, CHEN H. Criminal network analysis and visualization[J]. Communications of the ACM, 2005, 48(6): 100-107. DOI:10.1145/1064830. 1064834
doi: 10.1145/1064830. 1064834
|
3 |
GHOSH S, REILLY D L. Credit card fraud detection with a neural-network[C]// Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences. Wailea: IEEE, 1994: 621-630. DOI:10.1109/HICSS.1994.323314
doi: 10.1109/HICSS.1994.323314
|
4 |
SHEN A, TONG R, DENG Y. Application of classification models on credit card fraud detection[C]// International Conference on Service Systems & Service Management. Chengdu: IEEE, 2007:1-4. doi:10.1109/icsssm.2007.4280163
doi: 10.1109/icsssm.2007.4280163
|
5 |
CHAN P K, FAN W, PRODROMIDIS A L, et al. Distributed data mining in credit card fraud detection[J]. IEEE Intelligent Systems and Their Applications, 1999, 14(6): 67-74. DOI:10.1109/5254.809570
doi: 10.1109/5254.809570
|
6 |
WHEELER R, AITKEN S. Multiple algorithms for fraud detection[M]. London: Springer, 2000: 219-231. doi:10.1007/978-1-4471-0465-0_14
doi: 10.1007/978-1-4471-0465-0_14
|
7 |
MAES S, TUYLS K, VANSCHOENWINKEL B, et al. Credit card fraud detection using Bayesian and neural networks[C]//Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies. Cuba:ICSC-NAISO, 2002: 261-270.
|
8 |
SHEN A, TONG R, DENG Y. Application of classification models on credit card fraud detection [C]// International Conference on Service Systems & Service Management. Chengdu: IEEE, 2007:1-4. doi:10.1109/icsssm.2007.4280163
doi: 10.1109/icsssm.2007.4280163
|
9 |
BHATTACHARYYA S, JHA S, THARAKUNNEL K, et al. Data mining for credit card fraud: A comparative study[J]. Decision Support Systems, 2011, 50(3): 602-613. DOI:10.1016/j.dss.2010.08.008
doi: 10.1016/j.dss.2010.08.008
|
10 |
SRIVASTAVA A, KUNDU A, SURAL S, et al. Credit card fraud detection using hidden Markov model[J]. IEEE Transactions on Dependable and Secure Computing, 2008, 5(1): 37-48. DOI:10.1109/TDSC.2007.70228
doi: 10.1109/TDSC.2007.70228
|
11 |
SANCHEZ D, VILA M A, CERDA L, et al. Association rules applied to credit card fraud detection[J]. Expert Systems with Applications, 2009, 36(2): 3630-3640. DOI:10.1016/j.eswa. 2008.02.001
doi: 10.1016/j.eswa. 2008.02.001
|
12 |
BAHNSEN A C, STOJANOVIC A, AOUADA D, et al. Cost sensitive credit card fraud detection using Bayes minimum risk[C]// 2013 12th International Conference on Machine Learning and Applications. Miami: IEEE, 2013: 333-338. doi:10.1109/icmla.2013.68
doi: 10.1109/icmla.2013.68
|
13 |
BAHNSEN A C, STOJANOVIC A, AOUADA D, et al. Improving credit card fraud detection with calibrated probabilities[C]// Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia: Society for Industrial and Applied Mathematics, 2014: 677-685. doi:10.1137/1.9781611973440.78
doi: 10.1137/1.9781611973440.78
|
14 |
DUMAN E, ELIKUCUK I. Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization[C]// Proceedings of the 12th International Conference on Artificial Neural Networks. Puerto de la Cruz: Springer-Verlag, 2013: 62-71. DOI:10.1007/978-3-642-38682-4_8
doi: 10.1007/978-3-642-38682-4_8
|
15 |
MONAMO P M, MARIVATE V, TWALA B. Unsupervised learning for robust Bitcoin fraud detection[C]// 2016 Information Security for South Africa (ISSA). Johannesburg: IEEE, 2016: 129-134. DOI:10.1109/ISSA.2016.7802939
doi: 10.1109/ISSA.2016.7802939
|
16 |
MONAMO P M, MARIVATE V, TWALA B. A multifaceted approach to Bitcoin fraud detection: Global and local outliers[C]// 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim: IEEE, 2016: 188-194. DOI:10.1109/ICMLA.2016.0039
doi: 10.1109/ICMLA.2016.0039
|
17 |
RAI A K, DWIVEDI R K. Fraud detection in credit card data using unsupervised machine learning based scheme[C]//2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). Coimbatore: IEEE, 2020: 421-426. DOI:10.1109/ICESC48915.2020.9155615
doi: 10.1109/ICESC48915.2020.9155615
|
18 |
NIU X, WANG L, YANG X. A Comparison Study of Credit Card Fraud Detection: Supervised Versus Unsupervised[EB/OL]. [2019-04-24]. DOI:10. 48550/arXiv.1904.10604
doi: 10. 48550/arXiv.1904.10604
|
19 |
KLERKS P. The network paradigm applied to criminal organisations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands[J]. Transnational Organised Crime, 2001, 24(3): 53-65.
|
20 |
SPANN D D. Fraud Analytics: Strategies and Methods for Detection and Prevention[M]. New York: John Wiley & Sons Inc, 2012: 107-125. DOI:10.1002/9781118284049.ch8
doi: 10.1002/9781118284049.ch8
|
21 |
XU J J, CHEN H. CrimeNet explorer: A framework for criminal network knowledge discovery[J]. ACM Transactions on Information Systems, 2005, 23(2): 201-226. DOI:10.1145/1059981.1059984
doi: 10.1145/1059981.1059984
|
22 |
CHEN H, ZENG D, ATABAKHSH H, et al. COPLINK: Managing law enforcement data and knowledge[J]. Communications of the ACM, 2003, 46(1): 28-34. DOI:10.1145/602421.602441
doi: 10.1145/602421.602441
|
23 |
WASSERMAN S, FAUST K. Social Network Analysis: Methods and Applications[M]. New York: Cambridge University Press, 1994. DOI:10. 2307/2291756
doi: 10. 2307/2291756
|
24 |
BAJER W E, FAULKNER R R. The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry[J]. American Sociological Review, 1993,58(6): 837-860. DOI:10.2307/2095954
doi: 10.2307/2095954
|
25 |
KREBS V E. Mapping networks of terrorist cells[J]. Connections,2002,24(3):43-52.
|
26 |
PEROZZI B, Al-RFOU R, SKIENA S. Deepwalk: Online learning of social representations[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 701-710. DOI:10.1145/2623330.2623732
doi: 10.1145/2623330.2623732
|
27 |
GROVER A, LESKOVEC J. Node2vec: Scalable feature learning for networks[C]// Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 855-864. DOI:10.1145/2939672.2939754
doi: 10.1145/2939672.2939754
|
28 |
KIPF T N, WELLING M. Semi-Supervised Classification with Graph Convolutional Networks[EB/OL]. [2017-02-22]. DOI: 10.48550/arXiv. 1609. 02907
doi: 10.48550/arXiv. 1609. 02907
|
29 |
HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]// Proceedings of the 31th International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc, 2017: 1025-1035. doi:10.7551/mitpress/11474.003.0014
doi: 10.7551/mitpress/11474.003.0014
|
30 |
VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[EB/OL]. [2018-02-04]. DOI: 10.48550/arXiv.1710.10903
doi: 10.48550/arXiv.1710.10903
|
31 |
BEHERA T K, PANIGRAHI S. Credit card fraud detection: A hybrid approach using fuzzy clustering & neural network[C]// 2015 2nd International Conference on Advances in Computing and Communication Engineering. Dehradun: IEEE, 2015: 494-499. DOI:10.1109/ICACCE.2015.33
doi: 10.1109/ICACCE.2015.33
|
32 |
AKOGLU L, TONG H, KOUTRA D. Graph based anomaly detection and description: A survey[J]. Data Mining and Knowledge Discovery, 2015, 29(3): 626-688. DOI:10.1007/s10618-014-0365-y
doi: 10.1007/s10618-014-0365-y
|
33 |
UENO K, SUZUMURA T, MARUYAMA N, et al. Efficient breadth-first search on massively parallel and distributed-memory machines[J]. Data Science and Engineering, 2017, 2(1): 22-35. DOI:10.1007/s41019-016-0024-y
doi: 10.1007/s41019-016-0024-y
|
34 |
COLLADON A F, REMONDI E. Using social network analysis to prevent money laundering[J]. Expert Systems with Applications, 2017, 67: 49-58. DOI:10.1016/j.eswa.2016.09.029
doi: 10.1016/j.eswa.2016.09.029
|
35 |
BALTOIU A, PATRASCU A, IROFTI P. Community-Level Anomaly Detection for Anti-Money Laundering[EB/OL]. [2019-10-24]. DOI: 10.48550/arXiv.1910.11313
doi: 10.48550/arXiv.1910.11313
|
36 |
ZHOU R, ZHANG Q, ZHANG P, et al. Anomaly detection in dynamic attributed networks[J]. Neural Computing and Applications, 2021, 33(6): 2125-2136. DOI:10.1007/s00521-020-05091-3
doi: 10.1007/s00521-020-05091-3
|
37 |
SAVAGE D, WANG Q, CHOU P, et al. Detection of Money Laundering Groups Using Supervised Learning in Networks[EB/OL]. [2016-08-02]. DOI:10.48550/arXiv.1608.00708
doi: 10.48550/arXiv.1608.00708
|
38 |
LUCAS Y, PORTIER P E, LAPORTE L, et al. Multiple perspectives HMM-based feature engineering for credit card fraud detection[C]// Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. Limassol: ACM, 2019: 1359-1361. DOI:10.1145/3297280.3297586
doi: 10.1145/3297280.3297586
|
39 |
ZHU X J, GHAHRAMANI Z. Learning from labeled and unlabeled data with label propagation CMU-CALD-02-107 [R]. Pittsburgh: Carnegie Mellon University, 2002.
|
40 |
GIRVAN M, NEWMAN M E J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences, 2002, 99(12): 7821-7826. DOI:10.1073/pnas. 122653799
doi: 10.1073/pnas. 122653799
|
41 |
NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 026113. DOI:10. 1103/physreve.69.026113
doi: 10. 1103/physreve.69.026113
|
42 |
BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008. DOI:10.1088/1742-5468/2008/10/p10008
doi: 10.1088/1742-5468/2008/10/p10008
|
43 |
XIE J, SZYMANSKI B K, LIU X. SLPA: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process[C]// 2011 IEEE 11th International Conference on Data Mining Workshops. Vancouver: IEEE, 2011: 344-349. DOI:10.1109/ICDMW.2011.154
doi: 10.1109/ICDMW.2011.154
|
44 |
凡友荣, 杨涛, 孔华锋, 等. 基于知识图谱的电信欺诈通联特征挖掘方法[J]. 计算机应用与软件, 2019, 36(11): 182-187. doi:10.3969/j.issn.1000-386x.2019.11.030 FAN Y R, YANG T, KONG H F, et al. Telecommunication fraud communication feature mining method based on knowledge graph[J]. Computer Applications and Software, 2019, 36(11): 182-187. doi:10.3969/j.issn.1000-386x.2019.11.030
doi: 10.3969/j.issn.1000-386x.2019.11.030
|
45 |
CHANG Y C, LAI K T, CHOU S C T, et al. Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk[J]. Data Technologies and Applications, 2020, 55: 1-18. DOI:10.1108/dta-05-2020-0103
doi: 10.1108/dta-05-2020-0103
|
46 |
CAO B, MAO M, VIIDU S, et al. HitFraud: A broad learning approach for collective fraud detection in heterogeneous information networks[C]// 2017 IEEE International Conference on Data Mining (ICDM). New Orleans: IEEE, 2017: 769-774. DOI:10.1109/ICDM.2017.90
doi: 10.1109/ICDM.2017.90
|
47 |
YANG Y, XU Y, SUN Y, et al. Mining fraudsters and fraudulent strategies in large-scale mobile social networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(1): 169-179. DOI:10. 1109/tkde.2019.2924431
doi: 10. 1109/tkde.2019.2924431
|
48 |
YANG Y, XU Y, WANG C, et al. Understanding default behavior in online lending[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Beijing: ACM, 2019: 2043-2052. DOI:10.1145/3357384. 3358052
doi: 10.1145/3357384. 3358052
|
49 |
BURT R S. Structural Holes: The Social Structure of Competition[M]. Cambridge: Harvard University Press, 2009.
|
50 |
LIU X, WANG X. A network embedding based approach for telecommunications fraud detection[C]// International Conference on Cooperative Design, Visualization and Engineering. Shanghai: Springer, 2018: 229-236. DOI:10.1007/978-3-030-00560-3_31
doi: 10.1007/978-3-030-00560-3_31
|
51 |
LIU W, LIU Z, YU F, et al. A scalable attribute-aware network embedding system[J]. Neurocomputing, 2019, 339: 279-291. DOI:10.1016/j.neucom.2019.01.106
doi: 10.1016/j.neucom.2019.01.106
|
52 |
VAN BELLE R, VAN DSMME C, TYTGAT H, et al. Inductive graph representation learning for fraud detection[J]. Expert Systems with Applications, 2022, 193: 116463. DOI:10.1016/j.eswa.2021. 116463
doi: 10.1016/j.eswa.2021. 116463
|
53 |
GREGORY S. Finding overlapping communities in networks by label propagation[J]. New Journal of Physics, 2010, 12(10): 103018. DOI:10.1088/1367-2630/12/10/103018
doi: 10.1088/1367-2630/12/10/103018
|
54 |
PENG L, LIN R. Fraud phone calls analysis based on label propagation community detection algorithm[C]// 2018 IEEE World Congress on Services. San Francisco: IEEE, 2018: 23-24. DOI:10.1109/SERVICES.2018.00025
doi: 10.1109/SERVICES.2018.00025
|
55 |
HOSSEINI R, REZVANIAN A. AntLP: Ant‐based label propagation algorithm for community detection in social networks[J]. CAAI Transactions on Intelligence Technology, 2020, 5(1): 34-41. DOI:10.1049/trit.2019.0040
doi: 10.1049/trit.2019.0040
|
56 |
WANG J, GUO Y, WEN X, et al. Improving graph-based label propagation algorithm with group partition for fraud detection[J]. Applied Intelligence, 2020, 50: 3291-3300. DOI:10.1007/s10489-020-01724-1
doi: 10.1007/s10489-020-01724-1
|
57 |
ZHAO P, FU X, WU W, et al. Network-based feature extraction method for fraud detection via label propagation[C]// 2019 IEEE International Conference on Big Data and Smart Computing. Kyoto: IEEE, 2019: 1-6. DOI:10. 1109/BIGCOMP. 2019.8679414
doi: 10. 1109/BIGCOMP. 2019.8679414
|
58 |
YE C, LI Y, HE B, et al. GPU-accelerated graph label propagation for real-time fraud detection[C]// Proceedings of the 2021 International Conference on Management of Data. Xi'an: ACM, 2021: 2348-2356. DOI:10.1145/3448016.3452774
doi: 10.1145/3448016.3452774
|
59 |
YUAN S, WU X, LI J, et al. Spectrum-based deep neural networks for fraud detection[C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: ACM, 2017: 2419-2422. DOI:10.1145/3132847.3133139
doi: 10.1145/3132847.3133139
|
60 |
YING X, WU X, BARBARA D. Spectrum based fraud detection in social networks[C]// 2011 IEEE 27th International Conference on Data Engineering. Hannover: IEEE, 2011: 912-923. DOI:10.1109/ICDE.2011.5767910
doi: 10.1109/ICDE.2011.5767910
|
61 |
TROJA E, LIN I. Fraud-resilient privacy-preserving crowd-sensing for dynamic spectrum access[C]// 2021 IEEE 93th Vehicular Technology Conference. Helsinki: IEEE, 2021: 1-5. DOI:10.1109/VTC2021- Spring51267.2021.9448648
doi: 10.1109/VTC2021- Spring51267.2021.9448648
|
62 |
CHAMI I, ABU-El-HAIJA S, PEROZZI B, et al. Machine learning on graphs: A model and comprehensive taxonomy[J]. Journal of Machine Learning Research, 2022, 23(89): 1-64.
|
63 |
XU K, HU W, LESKOVEC J, et al. How Powerful are Graph Neural Networks? [EB/OL]. [2019-02-22]. DOI:10.48550/arXiv.1810.00826
doi: 10.48550/arXiv.1810.00826
|
64 |
JIANG J, CHEN J, GU T, et al. Anomaly detection with graph convolutional networks for insider threat and fraud detection[C]// MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM). Norfolk: IEEE, 2019: 109-114. DOI:10.1109/MILCOM47813.2019.9020760
doi: 10.1109/MILCOM47813.2019.9020760
|
65 |
LYU L, CHENG J, PENG N, et al. Auto-encoder based graph convolutional networks for online financial anti-fraud[C]// 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). Shenzhen: IEEE, 2019: 1-6. DOI:10.1109/CIFEr.2019. 8759109
doi: 10.1109/CIFEr.2019. 8759109
|
66 |
JIANG N, DUAN F, CHEN H, et al. MAFI: GNN-based multiple aggregators and feature interactions network for fraud detection over heterogeneous graph[J]. IEEE Transactions on Big Data, 2022, 8(4): 905-919. DOI:10.1109/TBDATA.2021.3132672
doi: 10.1109/TBDATA.2021.3132672
|
67 |
YANG K, XU W. Fraud memory: Explainable memory-enhanced sequential neural networks for financial fraud detection[C]// Hawaii International Conference on System Sciences, Hawaii: IEEE, 2019. DOI:10. 24251/HICSS.2019.126
doi: 10. 24251/HICSS.2019.126
|
68 |
WANG D, LIN J, CUI P, et al. A semi-supervised graph attentive network for financial fraud detection[C]// 2019 IEEE International Conference on Data Mining (ICDM). Beijing: IEEE, 2019: 598-607. DOI:10.1109/ICDM.2019.00070
doi: 10.1109/ICDM.2019.00070
|
69 |
QIN Z, LIU Y, HE Q, et al. Explainable graph-based fraud detection via neural meta-graph search[C]// Proceedings of the 31th ACM International Conference on Information & Knowledge Management. Atlanta: ACM, 2022: 4414-4418. DOI:10.1145/3511808.3557598
doi: 10.1145/3511808.3557598
|
70 |
WHITROW C, HAND D J, JUSZCZARK P, et al. Transaction aggregation as a strategy for credit card fraud detection[J]. Data Mining and Knowledge Discovery, 2009, 18(1): 30-55. DOI:10.1007/s10618-008-0116-z
doi: 10.1007/s10618-008-0116-z
|
71 |
NEIL D, PFEIFFER M, LIU S C. Phased LSTM: Accelerating recurrent network training for long or event-based sequences[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona: NIPS, 2016: 3889-3897. DOI:10.5167/UZH-149394
doi: 10.5167/UZH-149394
|