Network visualization for financial crime detection

Journal of Visual Languages and Computing

Volumn 25, Issue 4, 2014, Pages 433-451

  Didimo, Walter ^a   Liotta, Giuseppe ^a   Montecchiani, Fabrizio ^a  

^a UNIVERSITY OF PERUGIA   (Italy)

Author keywords

Financial crime detection; Graph visualization; Information visualization; Social network analysis

Indexed keywords

EID: 84904761793     PISSN: 1045926X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jvlc.2014.01.002     Document Type: Article

References (43)

1. Abello J., van Ham F., Krishnan N. Ask-graphview. a large scale graph visualization system. IEEE Trans. Vis. Comput. Gr. 2006, 12(5):669-676.
2. Altaf-Ul-Amin M., Nishikata K., Koma T., Miyasato T., Shinbo Y., Arifuzzaman M., Wada C., Maeda M., Oshima T., Mori H., Kanaya S. Prediction of protein functions based on K-cores of protein-protein interaction networks and amino acid sequences. Genome Inf. 2003, 14:498-499.
3. Anderson T., Arbetter L., Benawides L., Longmore-Etheridge A. Security works. Secur. Manag. 1994, 38(17):17-20.
4. Archambault D., Munzner T., Auber D. Grouseflocks. steerable exploration of graph hierarchy space. IEEE Trans. Vis. Comput. Gr. 2008, 14(4):900-913.
5. D. Auber, Y. Chiricota, F. Jourdan, G. Melançon, Multiscale visualization of small world networks, in: IEEE Information Visualization (INFOVIS 2003), 2003, pp. 75-81.
6. Batagelj V., Brandenburg F.-J., Didimo W., Liotta G., Palladino P., Patrignani M. Visual analysis of large graphs using (X,Y)-clustering and hybrid visualizations. IEEE Trans. Vis. Comput. Gr. 2011, 17(11):1587-1598.
7. V. Batagelj, A. Mrvar, M. Zaveršnik, Partitioning approach to visualization of large networks, in: Graph Drawing (GD 1999), Lecture Notes in Computer Science, vol. 1731, 1999, pp. 90-97.
8. V. Batagelj, M. Zaversnik, An O(m) Algorithm for Cores Decomposition of Networks, CoRR, cs.DS/0310049, 2003.
9. B. Bollobás, The evolution of sparse graphs, in: A. Press (Ed.), Graph Theory and Combinatorics, 1984, pp. 35-57.
10. Carmignani A., Di Battista G., Didimo W., Matera F., Pizzonia M. Visualization of the high level structure of the internet with HERMES. J. Gr. Algorithms Appl. 2002, 6(3):281-311.
11. Chang R., et al. Scalable and interactive visual analysis of financial wire transactions for fraud detection. Inf. Vis. 2008, 7(1):63-76.
12. H. Chen, H. Atabakhsh, C. Tseng, B. Marshall, S. Kaza, S. Eggers, H. Gowda, A. Shah, T. Petersen, C. Violette, Visualization in law enforcement, in: ACM Conference on Human Factors in Computing Systems (CHI 2005), ACM, 2005, pp. 1268-1271.
13. Chen H., Zeng D.D., Atabakhsh H., Wyzga W., Schroeder J. Coplink. managing law enforcement data and knowledge. Commun. ACM 2003, 46(1):28-34.
14. Di Battista G., Eades P., Tamassia R., Tollis I.G. Graph Drawing 1999, Prentice Hall, Upper Saddle River, NJ.
15. Di Giacomo E., Didimo W., Grilli L., Liotta G. Graph visualization techniques for web clustering engines. IEEE Trans. Vis. Comput. Gr. 2007, 13(2):294-304.
16. E. Di Giacomo, W. Didimo, G. Liotta, P. Palladino, Visual analysis of financial crimes, in: Advanced Visual Interface (AVI 2010), ACM, 2010, pp. 393-394.
17. Didimo W., Liotta G. Mining Graph Data, Chapter Graph Visualization and Data Mining 2007, 35-64. Wiley, Hoboken, New Jersey.
18. W. Didimo, F. Montecchiani, Fast layout computation of clustered networks: algorithmic advances and experimental analysis, Inf. Sci., 260 (2014) 185-199.
19. W. Didimo, F. Montecchiani, Fast layout computation of hierarchically clustered networks: algorithmic advances and experimental analysis, in: Information Visualization (IV 2012), IEEE, 2012, pp. 18-23.
20. H. A. do Nascimento, P. Eades, A system for graph clustering based on user hints, in: Visual Information Processing (VIP 2000), vol. 2 of CRPIT, ACS, 2001, pp. 73-74.
21. Dogrusoz U., Giral E., Cetintas A., Civril A., Demir E. A layout algorithm for undirected compound graphs. Inf. Sci. 2009, 179:980-994.
22. S.N. Dorogovtsev, A.V. Goltsev, J.F.F. Mendes, k-Core Organization of Complex Networks, CoRR, , 2005. http://abs/cond-mat/0509102.
23. P. Eades, A heuristic for graph drawing, in: Congressus Numerantium, vol. 42, 1984, pp. 149-160.
24. P. Eades, R. F. Cohen, M. L. Huang, Online animated graph drawing for web navigation, in: Graph Drawing (GD 1997), vol. 1353, 1997, pp. 330-335.
25. Eades P., Huang M.L. Navigating clustered graphs using force-directed methods. J. Gr. Algorithms Appl. 2000, 4(3):157-181.
26. FinCEN-United States Department of the Treasury, Guidance on Preparing a Complete & Sufficient Suspicious Activity Report Narrative 2003. http://www.fincen.gov/.
27. Freeman L.C. A set of measures of centrality based on betweenness. Sociometry 1977, 40:35-41.
28. Y. Frishman, A. Tal, Dynamic drawing of clustered graphs, in: IEEE Information Visualization, IEEE, 2004, pp. 191-198.
29. Fruchterman T.M.J., Reingold E.M. Graph drawing by force-directed placement. Softw. Pract. Exp. 1991, 21(11):1129-1164.
30. H.G. Goldberg, T.E. Senator, Restructuring databases for knowledge discovery by consolidation and link formation, in: Knowledge Discovery and Data Mining (KDD 1995), AAAI Press, 1995, pp. 136-141.
31. A.V. Goltsev, S. N. Dorogovtsev, J. F. F. Mendes, k-core (bootstrap) percolation on complex networks, in: Critical Phenomena and Nonlocal Effects, CoRR, , 2006. http://abs/cond-mat/0602611.
32. Klerks P., Smeets E. The network paradigm applied to criminal organizations: theoretical nitpicking or a relevant doctrine for investigators? Recent developments in The Netherlands. Connections 2001, 24:53-65.
33. Latora V., Marchiori M. A measure of centrality based on the network efficiency. New J. Phys. 2007, 9.
34. N. Memon, H.L. Larsen, Structural analysis and destabilizing terrorist networks, in: Data Mining (DMIN 2006), CSREA Press, 2006, pp. 296-302.
35. E.L. Merrer, G. Trédan, Centralities: capturing the fuzzy notion of importance in social graphs, in: Social Network Systems (SNS 2009), ACM, 2009, pp. 33-38.
36. Schaffer D., Zuo Z., Greenberg S., Bartram L., Dill J., Dubs S., Roseman M. Navigating hierarchically clustered networks through fisheye and full-zoom methods. ACM Trans. Comput.-Human Interact. 1996, 3(2):162-188.
37. Seidman S.B. Network structure and minimum degree. Social Netw. 1983, 5:269-287.
38. Stasko J., Görg C., Liu Z. Jigsaw: supporting investigative analysis through interactive visualization. Inf. Vis. 2008, 7(April):118-132.
39. Tamassia R. Constraints in graph drawing algorithms. Constraints 1998, 3(April):87-120.
40. L. Tang, G. Barbier, H. Liu, J. Zhang, A social network analysis approach to detecting suspicious online financial activities, in: Advances in Social Computing, LNCS, 2010, pp. 390-397.
41. F. van Ham, J. J. van Wijk, Interactive visualization of small world graphs, in: IEEE Information Visualization (INFOVIS 2004), 2004, pp. 199-206.
42. Westphal C. Data Mining for Intelligence, Fraud, & Criminal Detection 2009, CRC Press, Boca Raton, FL.
43. Xu J., Chen H. Criminal network analysis and visualization. Commun. ACM 2005, 48(6):101-107.