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Mining Blackhole and Volcano Patterns for Fraud Detection

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Encyclopedia of Social Network Analysis and Mining
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Synonyms

Blackhole pattern; Financial fraud detection; Graph mining; Network structure analysis; Volcano pattern

Glossary

Directed Graph:

A set of nodes and edges connecting the nodes with directions

Path:

An alternating sequence of nodes and edges that begins from a start node and ends at an end node, such that from each of its nodes (expect for the end node) there is an edge to the next node in the sequence

Definition

Consider a directed graph G = (V, E) (Diestel 2005), where V is the set of all nodes and E is the set of all edges. Assume that G has no self-loops. A directed edge e in G is denoted as e = (x, y), where x and y are nodes of G and an arc is directed from x to y. Each edge e has a positive weight, denoted ωe, associated with it.

Connected/Weakly Connected

In an undirected graph G, two nodes u and Ï… are called connected if G contains a path from u to Ï…. An undirected graph is called connected if every pair of distinct nodes in the graph is connected. A directed graph is...

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References

  • Adamic L, Brunetti C, Harris J, Kirilenko A (2010) Trading networks. SSRN eLibrary, http://ssrn.com/paper=1361184. Accessed 20 Dec 2012

  • Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: spotting anomalies in weighted graphs. In: Proceedings of the 14th pacific-asia conference on knowledge discovery and data mining (PAKDD’ 10), Hyderabad, pp 410–421

    Chapter  Google Scholar 

  • Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, New York. MATH

  • Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data (ACM SIGMOD’00), Dallas, pp 93–104

    Google Scholar 

  • Chakrabarti D (2004) Autopart: parameter-free graph partitioning and outlier detection. In: Proceedings of the 8th european conference on principles and practice of knowledge discovery in databases (PKDD’04), Pisa, pp 112–124

    Google Scholar 

  • Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Philadelphia, pp 554–560

    Chapter  Google Scholar 

  • Chaudhary A, Szalay AS, Moore AW (2002) Very fast outlier detection in large multidimensional data sets. In: Proceedings of the ACM SIGMOD workshop in research issues in data mining and knowledge discovery (DMKD), Madison

    Google Scholar 

  • Cook DJ, Holder LB (1994) Substructure discovery using minimum description length and background knowledge. J Artif Intell Res 1:231–255

    Google Scholar 

  • Diestel R (2005) Graph theory (graduate texts in mathematics). Springer, New York

    Google Scholar 

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174. MathSciNet

    Article  MathSciNet  Google Scholar 

  • Ghosh R, Lerman K (2008) Community detection using a measure of global influence. In: The 2nd SNAKDD workshop on social network mining and analysis (SNA-KDD’08), Las Vegas

    Google Scholar 

  • Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826. MATHMathSciNet

    Article  MathSciNet  MATH  Google Scholar 

  • Hawkins D (1980) Identification of outliers. Chapman and Hall, New York. MATH

  • Hopcroft J, Khan O, Kulis B, Selman B (2003) Natural communities in large linked networks. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (ACM SIGKDD’03), Washington

    Google Scholar 

  • Huan J, Wang W, Prins J (2003) Efficient mining of frequent subgraphs in the presence of isomorphism. In: Proceedings of the 3rd IEEE international conference on data mining (IEEE ICDM’03), Melbourne

    Google Scholar 

  • Jiang X, Xiong H, Wang C, Tan AH (2009) Mining globally distributed frequent subgraphs in a single labeled graph. Data Knowl Eng 68:1034–1058

    Article  Google Scholar 

  • Jiang C, Coenen F, Zito M (2012) A survey of frequent subgraph mining algorithms. Knowl Eng Rev (To appear)

    Article  Google Scholar 

  • Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis. Pearson Prentice Hall, Upper Saddle River. MATH

  • Knuth D (2011) The art of computer programming, volume 4A: combinatorial algorithms, part 1. Addison-Wesley, Upper Saddle River

    MATH  Google Scholar 

  • Kuramochi M, Karypis G (2005) Finding frequent patterns in a large sparse graph. Data Min Knowl Discov 11(3):243–271. MathSciNet

    Article  MathSciNet  Google Scholar 

  • Lazarevic A, Kumar V (2005) Feature bagging for outlier detection. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining (ACM SIGKDD’05), Chicago, pp 157–166

    Google Scholar 

  • Li Z, Xiong H, Liu Y, Zhou A (2010) Detecting blackhole and volcano patterns in directed networks. In: Proceedings of the 10th IEEE international conference on data mining (IEEE ICDM’10), Sydney, pp 294–303

    Google Scholar 

  • Li Z, Xiong H, Liu Y (2012) Mining blackhole and volcano patterns in directed graphs: a general approach. Data Min Knowl Discov 25(3):577–602. MathSciNet

    Article  MathSciNet  MATH  Google Scholar 

  • Moonesinghe HDK, Tan P-N (2008) Outrank: a graph-based outlier detection framework using random walk. Int J Artif Intell Tools 17(1):19–36

    Article  Google Scholar 

  • Newman MEJ (2004) Detecting community structure in networks. Eur Phys J B 38:321–330

    Article  Google Scholar 

  • Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113

    Article  Google Scholar 

  • Noble CC, Cook DJ (2003) Graph-based anomaly detection. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (ACM SIGKDD’03), Washington, pp 631–636

    Google Scholar 

  • Pandit S, Chau DH, Wang S, Faloutsos C (2007) Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th international conference on World Wide Web, Banff. ACM, pp 201–210

    Google Scholar 

  • Papadimitriou S, Kitagawa H, Gibbons PB, Faloutsos C (2003) Loci: fast outlier detection using the local correlation integral. In: Proceedings of the 19th international conference on data engineering (ICDE’03), Bangalore, pp 315–326

    Google Scholar 

  • Pathak N, DeLong C, Banerjee A, Erickson K (2008) Social topic models for community extraction. In: The 2nd SNA-KDD workshop on social network mining and analysis (SNA-KDD’08), Las Vegas

    Google Scholar 

  • Steyvers M, Smyth P, Rosen-Zvi M, Griffiths T (2004) Probabilistic author-topic models for information discovery. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (ACM SIGKDD’04), Seattle

    Google Scholar 

  • Sun J, Qu H, Chakrabarti D, Faloutsos C (2005) Neighborhood formation and anomaly detection in bipartite graph. In: Proceedings of the 5th IEEE international conference on data mining (IEEE ICDM’05), Leipzig, pp 418–425

    Google Scholar 

  • Wang C, Wang W, Pei J, Zhu Y, Shi B (2004) Scalable mining of large disk-based graph databases. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (ACM SIGKDD’04), Seattle

    Google Scholar 

  • Wang J, Hsu W, Lee M, Sheng C (2006) A partition-based approach to graph mining. In: Proceedings of the 22nd international conference on data engineering (ICDE’06), Atlanta, p 74

    Google Scholar 

  • Yan X, Han J (2002) gspan: graph-based substructure pattern mining. In: Proceedings of the 2nd IEEE international conference on data mining (IEEE ICDM’02), Leipzig

    Google Scholar 

  • Zhou D, Manavoglu E, Li J, Giles CL, Zha H (2006) Probabilistic models for discovering e-communities. In: Proceedings of the 15th international world wide web conference (WWW’06), Edinburgh

    Google Scholar 

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Acknowledgments

This entry was supported by National Science Foundation (NSF) via grant numbers CCF-1018151, IIS-1256016, and DUE-1241315.

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Correspondence to Zhongmou Li .

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Li, Z., Xiong, H. (2018). Mining Blackhole and Volcano Patterns for Fraud Detection. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_282

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