Data Analysis Based Construction and Evolution of Terrorist and Criminal Networks

  • Khaled Dawoud
  • Tamer N. Jarada
  • Wadhah Almansoori
  • Alan Chen
  • Shang Gao
  • Reda Alhajj
  • Jon Rokne
Chapter

Abstract

The wide-spread usage of network and graph based approaches in modeling data has been approved to be effective for various applications. The network based framework becomes more powerful when it is expanded to benefit from the widely available techniques for data mining and machine learning which allow for effective knowledge discovery from the investigated domain. The underlying reason for the substantial efficacy in studying graphs, either directly (i.e., data is given in graph format, for example, the “phone-call” network in studying social evolutions) or indirectly (network is inferred from data by predefined method or scheme, such as co-occurrence network for studying genetic behaviors), is the fact that graph structures emphasize the intrinsic relationship between entities, i.e., nodes (or vertices) in the network (in this chapter, the terms network and graph are used interchangeably). For the indirect case information extraction techniques may be adapted to investigate open sources of data in order to derive the required network structure as reflected in the current available data. This is a tedious process but effective and could lead to more realistic and up-to-date information reflected in the network. The latter network will lead to better and close to real-time knowledge discovery in case online information extraction is affordable and provided. Estimating network structure has attracted the attention of other researchers involved in terrorist network analysis, e.g.[9].

Keywords

Volatility 

References

  1. 1.
  2. 2.
    Albanese M, Subrahmanian VS (2007) T-REX: A System for Automated Cultural Information Extraction. In Proceedings of the First International Conference on Computational Cultural Dynamics (ICCCD 2007), pp 2–8, AAAI Press, College Park, Maryland, USA, August 27–28Google Scholar
  3. 3.
    Albert I, Albert R (2004) Conserved network motifs allow protein-protein interaction prediction. Bioinformatics 20(18):3346–3352Google Scholar
  4. 4.
    Almansoori W, Gao S, Jarada TN, Elsheikh AM, Murshed AN, Jida J, Alhajj R, Rokne J (2012) Link prediction and classification in social networks and its application in healthcare and systems biology. Netw Model Anal Health Inf Bioinf 1(1–2):27–36Google Scholar
  5. 5.
    Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In Proceedings of the fourth ACM international conference on Web search and data mining. ACM, New York, pp 635–644Google Scholar
  6. 6.
    Chen ACL, Gao S, Karampelas P, Alhajj R, Rokne JG (2011) Finding hidden links in terrorist networks by checking indirect links of different sub-networks. In: Wiil UK (ed) Counterterrorism and open source intelligence. Springer, Wien/New York, pp 143–158Google Scholar
  7. 7.
    Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98–101Google Scholar
  8. 8.
    Dawoud K, Alhajj R, Rokne JG (2010) A global measure for estimating the degree of organization of terrorist networks. In: Proceedings of advances in social networks analysis and mining. IEEE Computer Society Washington, DC, pp 421–427Google Scholar
  9. 9.
    Dombroski MJ, Carley KM (2002) Netest: estimating a terrorist network’s structure. Comput Math Organ Theory 8(3):235–241Google Scholar
  10. 10.
    Dunlavy DM, Kolda TG, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data 5(2):1–27Google Scholar
  11. 11.
    Farely D (2003) Breaking al qaeda cells: A mathematical analysis of counterterrorism operations. Stud Confl Terror 26(6):399–411Google Scholar
  12. 12.
    Garreau J (2001) Disconnect the dots: maybe we can’t cut off terror’s head but we can take out its nodes. In: Washington Post Online. http://edge.org/response-detail/2328/what-now-
  13. 13.
    Getoor L, Diehl CP (2005) Link mining: a survey. SIGKDD Explor Newsl 7(2):3–12Google Scholar
  14. 14.
    Jure Leskovec DH, Kleinberg J (2010) Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web (WWW ’10). ACM, New York, pp 641–650Google Scholar
  15. 15.
    Karypis G, Kumar V (1995) Unstructured graph partitioning and sparse matrix ordering system. http://dm.kaist.ac.kr/kse625/resources/metis.pdf
  16. 16.
    Klerks P (2001) The network paradigm applied to criminal organizations. Connections 24(3):53–65Google Scholar
  17. 17.
    Krebs V (2002) Mapping networks of terrorist cells. Connections 24:43–52Google Scholar
  18. 18.
    Latora V, Marchiori M (2004) How the science of complex networks can help developing strategies against terrorism. Chaos Solitons Fractals 20(1):69–75Google Scholar
  19. 19.
    Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031Google Scholar
  20. 20.
    Magdon-Ismail M, Goldberg M, Wallace W, Siebecker D (2010) Locating hidden groups in communication networks using hidden markov models. In: Chen H, Miranda R, Zeng D, Demchak C, Schroeder J, Madhusudan T (eds) Intelligence and security informatics. Lecture notes in computer science, vol 2665. Springer, Berlin/Heidelberg, p 958Google Scholar
  21. 21.
    Qin J, Xu J, Hu D, Sageman M, Chen H (2005) Analyzing terrorist networks: a case study of the global salafi jihad network. Lecture Notes in Computer Science, 2005, Vol 3495, pp 287–304Google Scholar
  22. 22.
    Ressler S (2006) Social network analysis as an approach to combat terrorism: past, present, and future research. Homeland Security Affairs 2(2)Google Scholar
  23. 23.
    Santos EE, Pan L, Arendt D, Pittkin M (2006) An effective anytime anywhere parallel approach for centrality measurements in social network analysis. In: IEEE international conference on systems, man and cybernetics, 2006. SMC ’06, vol 6. pp 4693–4698, IEEE, Taipei, Taiwan, October 8-11, 2006Google Scholar
  24. 24.
    Shaikh M, Wang J (2006) Discovering hierarchical structure in terrorist networks. In: Proceedings of the international conference on emerging technologies. pp 238–244. IEEE, Peshawar, Pakistan, 13-14 November 2006Google Scholar
  25. 25.
    Sparrow M (1991) The application of network analysis to criminal intelligence: An assessment of the prospects. Soc Netw 13(3):251–274Google Scholar
  26. 26.
    Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–276 (2001)Google Scholar
  27. 27.
    Tylenda T, Angelova R, Bedathur S (2009) Towards time-aware link prediction in evolving social networks. In: Proceedings of the 3rd workshop on social network mining and analysis, SNA-KDD ’09. ACM, New York, pp 1–10Google Scholar
  28. 28.
    Xu JJ, Chen H (2005) CrimeNet explorer: a framework for criminal network knowledge discovery. ACM Trans Inf Syst 23(2):201–226. http://dx.doi.org/10.1145/1059981.1059984 Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Khaled Dawoud
    • 1
  • Tamer N. Jarada
    • 1
  • Wadhah Almansoori
    • 1
  • Alan Chen
    • 1
  • Shang Gao
    • 1
  • Reda Alhajj
    • 1
    • 2
  • Jon Rokne
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Department of Computer ScienceGlobal UniversityBeirutLebanon

Personalised recommendations