Detecting blackhole and volcano patterns in directed networks

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 294-303

  Li, Zhongmou ^a   Xiong, Hui ^a   Liu, Yanchi ^a,b   Zhou, Aoying ^c  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^c EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Blackhole pattern; Fraud detection; Graph mining; Network model; Volcano pattern

Indexed keywords

BLACK HOLES; COMPUTATIONAL ADVANTAGES; DC ALGORITHM; DIRECTED GRAPHS; DIRECTED NETWORK; DIVIDE AND CONQUER; DUAL PROBLEM; FORCE METHOD; FRAUD DETECTION; GRAPH MINING; INLINKS; LARGE GRAPHS; MINING PROBLEMS; NETWORK MODEL; ORDERS OF MAGNITUDE; REAL WORLD DATA; SEARCH SPACES; SUBGRAPHS; TRADING NETWORKS;

ALGORITHMS; COMPUTER CRIME; CRIME; DATA MINING; VOLCANOES;

GRAPH THEORY;

EID: 79951738205     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.37     Document Type: Conference Paper

References (19)

1. R. Diestel, Graph Theory (Graduate Texts in Mathematics). Springer, 2006.
2. J. Leskovec, K. Lang, and et al, "Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters," in arXiv:0810.1355, 2008.
3. J. Leskovec, L. Adamic, and B. Adamic, "The dynamics of viral marketing," ACM TWEB, vol. 1, 2007.
4. V. Batagelj and A. Mrvar. Pajek datasets, 2006. http://vlado.fmf.uni-lj. si/pub/networks/data.
5. Wharton Research Data Services, University of Pennsylvania. https://wrds.wharton.upenn.edu/wrdsauth/members.cgi.
6. V. Boginski, S. Butenko, and P. M. Pardalos, "Statistical analysis of financial networks," Computational Stat. and Data Analysis, vol. 48, pp. 431-443, 2005.
7. W. Nooy, A. Mrvar, and V. Batagelj, Exploratory Social Network Analysis with Pajek. Cambridge University Press, 2005.
8. X. Jiang, H. Xiong, C. Wang, and A. H. Tan, "Mining globally distributed frequent subgraphs in a single labeled graph," Data and Knowledge Engineering, vol. 68, pp. 1034-1058, 2009.
9. X. Yan and J. Han, "gspan: Graph-based substructure pattern mining." in IEEE ICDM'02, 2002.
10. C. Wang, W. Wang, J. Pei, Y. Zhu, and B. Shi, "Scalable mining of large disk-based graph databases," in ACM SIGKDD'04, 2004.
11. J. Huan, W. Wang, and J. Prins, "Efficient mining of frequent subgraphs in the presence of isomorphism," in IEEE ICDM'03, 2003.
12. J. Wang, W. Hsu, M. Lee, and C. Sheng, "A partition-based approach to graph mining," in ICDE 2006, 2006, p. 74. (Pubitemid 44539866)
13. M. Kuramochi and G. Karypis, "Finding frequent patterns in a large sparse graph." Data Min. Knowl. Discov., vol. 11, no. 3, pp. 243-271, 2005. (Pubitemid 41669194)
14. D. J. Cook and L. B. Holder, "Substructure discovery using minimum description length and background knowledge." J. Artif. Intell. Res. (JAIR), vol. 1, pp. 231-255, 1994.
15. M. E. J. Newman, "Detecting community structure in networks," Eur. Phys. J. B, vol. 38, pp. 321-330, 2004.
16. M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Phys. Rev. E 69, vol. 026113, 2004.
17. M. Girvan and M. E. J. Newman, "Community structure in social and biological networks," in Proc. National Acad. Science, 2002.
18. J. Hopcroft, O. Khan, and et al, "Natural communities in large linked networks," in ACM SIGKDD'03, 2003.
19. R. Ghosh and K. Lerman, "Community detection using a measure of global influence," in SNA-KDD'08, 2008.