Skip to main content
SpringerLink
Log in
Book cover

Handbook of Social Network Technologies and Applications pp 523–547Cite as

Investigation of Key-Player Problem in Terrorist Networks Using Bayes Conditional Probability

  • Chapter
  • First Online:

  • 3075 Accesses

Abstract

In Social Network Analysis (SNA) it is quite conventional to employ graph theory concepts for example cut-points and cut-sets or measuring node centralities like betweenness, degree and closeness to highlight important actors in the network. However, it is also believed that most of these measures alone are inadequate for investigating terrorist/covert networks. In this paper we compute posterior probability using Bayes conditional probability theorem to compute each nodes positional probability to see how probable/likely that the node is a key player. Obviously, larger the probability value higher is the chances that the node is a key actor, the system after words using the computed probability can select an arbitrary number of nodes for elimination to make the network non-functional or severely destroying its capability. We know that that network fragmentation in its simplest form is a count of the number of components, more the number of counts larger is the fragmentation. The reason of calling it simple is because it does not include many aspects of the network for example structure etc. Borgatti has provided a similar measure which does includes the shape and internal structure of the components and high light the important actors in the network. Our computational procedure provides very similar results in highlighting the key player problem. We simulated our computational model through various random networks and we also applied it to the David Krackhardt’s kite network and to 9-11 hijackers network to bench mark our results. It shows that conceptually this framework of evaluation can be used to highlight the key players in terrorist/covert cells.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Borgatti S. P:. Identifying sets of key players in a network. Computational, Mathematical and Organizational Theory 12(1), pages 21–34, 2006.

    Article  MATH  Google Scholar 

  2. Borgatti S. P:. The key player problem. In R. Breiger, K. Carley, and P. Pattison, (eds.), Dynamic social network modeling and analysis, workshop summary and papers. National Academy of Sciences Press, pages 241–252, 2003.

    Google Scholar 

  3. Borgatti S. P. and M. G. Everett:. The centrality of groups and classes. Journal of Mathematical Sociology 23(3), pages 181–201, 1999.

    Article  MATH  Google Scholar 

  4. Valdis Krebs:. Connecting the dots, tracking two identified terrorists, 2002.

    Google Scholar 

  5. Bavelas A:. A mathematical model for group structures. Human Organization 7, pages 16–30, 1948.

    Article  Google Scholar 

  6. Shaw M. E:. Group structure and the behaviour of individuals in small groups. Journal of Psychology 38, pages 139–149, 1954.

    Article  Google Scholar 

  7. Bavelas A:. Communication patterns in task oriented groups. Journal of the Acoustical Society of America 22, pages 271–282, 1950.

    Article  Google Scholar 

  8. Leavitt Harold J:. Some effects of communication patterns on group performance. Journal of Abnormal and Social Psychology 46, pages 38–50, 1951.

    Article  Google Scholar 

  9. Smith Sidney L:. Communication pattern and the adaptability of task-oriented groups: an experimental study. Cambridge, MA, Group networks laboratory, research laboratory of electronics, Massachusetts Institute of Technology, 1950.

    Google Scholar 

  10. Bavelas A. and D. Barrett:. An expermental approach to organizational communication. Personnel 27, pages 366–371, 1951.

    Google Scholar 

  11. Glanzer M. and R. Glaser:. Techniques for the study of team structure and behaviour. Part II: Empirical studies of the effects of structure. Technical report, Pittsburgh, American Institute, 1957.

    Google Scholar 

  12. Glanzer M. and R. Glaser:. Techniques for the study of group structure and behaviour. Part II: Empirical studies of the effects of structure in small groups. Psychological Bulletin 58, pages l–27, 1961.

    Google Scholar 

  13. Cohen A. M:. Communication networks in research and training. Personnel Administration 27, pages 18–24, 1964.

    Google Scholar 

  14. Shaw M. E:. Communication networks. In L. Berkowitz (ed.), Advances in experimental social psychology, vol. vi, pages 111–147, New York, Academic Press, 1964.

    Google Scholar 

  15. Stephenson K. A. and M. Zelen:. Rethinking centrality: methods and examples. Social Networks 11, pages 1–37, 1989.

    Article  MathSciNet  Google Scholar 

  16. Flament C:. Applications of graph theory to group structure. Englewood Cliffs, NJ, Prentice Hall, 1963.

    Google Scholar 

  17. Burgess R. L:. Communication networks and behavioural consequences. Human Relations 22, pages 137–l59, 1968.

    Article  Google Scholar 

  18. Snadowski A:. Communication network research: an examination of controversies. Human Relations 25, pages 283–306, 1972.

    Article  Google Scholar 

  19. Rogers D. L:. Socio-metric analysis of inter-organizational relations: application of theory and measurement. Rural Socioeonv 39, pages 487–503, 1974.

    Google Scholar 

  20. Rogers D. L:. Communication networks in organizations. Communication in organizations, pages 108–148, New York, Free Press, 1976.

    Google Scholar 

  21. Cohn B. S. and M. Marriott:. Networks and centres of integration in indian civilization. Journal of Social Research I, pages 1–9, 1958.

    Google Scholar 

  22. Pitts F. R:. A graph theoretic approach to historical geography. The Professional Geographer 17, pages 15–20, 1965.

    Article  Google Scholar 

  23. Latora V. and M. Marchiori:. A measure of centrality based on network efficiency, arxiv.org preprint cond-mat/0402050, 2004.

    Google Scholar 

  24. Freeman Linton C:. A set of measures of centrality based on betweenness. Sociometry 40, pages 35–41, 1971.

    Article  Google Scholar 

  25. Freeman Linton C:. Centrality in social networks: conceptual clarification. Social Networks 1, page 215–239, 1979.

    Google Scholar 

  26. Anthonisse J. M:. The rush in a graph, University of Amsterdam Mathematical Centre, Amsterdam, 1971.

    Google Scholar 

  27. Nieminen J:. On centrality in a graph. Scandinavian Journal of Psychology 15, pages 322–336, 1974.

    Article  Google Scholar 

  28. Scott J:. Social networks analysis, 2nd edition, London, Sage Publications, 2003.

    Google Scholar 

  29. Sabidussi G:. The centrality index of a graph. Psychometrika 31, pages 581–603, 1966.

    Article  MathSciNet  MATH  Google Scholar 

  30. Shaw M. J., C. Subramaniam, G. W. Tan and M. E. Welge:. Knowledge management and data mining for marketing. Decision Support Systems 31(1), pages 127–137, 2001.

    Article  Google Scholar 

  31. Carley K. M., J.-S. Lee and D. Krackhardt:. Destabilizing networks, Dept. of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15143, November 2001.

    Google Scholar 

  32. Akbar Hussain D. M:. Destabilization of terrorist networks through argument driven hypothesis model. Journal of Software 2(6), pages 22–29, 2007.

    Google Scholar 

  33. Akbar Hussain D. M. and D. Ortiz-Arroy:. Locating key actors in social networks using bayes posterior probability framework, lecture notes in computer science. In Intelligence and Security Informatics, vol. 5376/2008.

    Google Scholar 

  34. Ortiz-Arroy D. and D. M. Akbar Hussain:. An information theory approach to identify sets of key players. In Intelligence and Security Informatics, vol. 5376/2008.

    Google Scholar 

  35. Adibi J. and J. Shetty:. Discovering important nodes through graph entropy the case of enron email database. In Linkkdd 2005: Proceedings of the 3rd international workshop on link discovery, pages 74–81, ACM, New York, 2005.

    Google Scholar 

  36. Freeman L. C., S. P. Borgatti and D. R. White:. Centrality in valued graphs, a measure of betweenness based on network flow. Social Networks 13, pages 141–154, 1991.

    Article  MathSciNet  Google Scholar 

  37. Newman M. E. J:. A measure of betweenness centrality based on random walks, cond-mat/0309045, 2003.

    Google Scholar 

  38. Beauchamp M. A:. An improved index of centrality. Behavioral Science 10, pages 161–163, 1965.

    Article  Google Scholar 

  39. Oliver C. Ibe:. Fundamentals of applied probability and random processes, Elsevier Academics Press, ISBN 0-12-088508-5, 2005.

    Google Scholar 

  40. Montgomery D. C. and G. C. Runger:. Applied statistics abd probability for engineers, 4th edition, ISBN 978-0-471-74589-1, Wiley, 2006.

    Google Scholar 

  41. Koskinen J. H. and T. A. B. Snijders:. Bayesian inference for dynamic social network data. Journal of Statistical Planning and Inference 137, pages 3930–3938, 2007.

    Article  MathSciNet  MATH  Google Scholar 

  42. Siddarth K., H. Daning and C. Hsinchum:. Dynamic social network analysis of a dark network: identifying significant facilitators. In Proceedings of IEEE international conference on intelligence and security informatics, ISI 2007, New Brunswick, NJ, USA, May 23–24, 2007.

    Google Scholar 

  43. C. J. Rhodes and E. M. J. Keefe:. Social network topology: a bayesian approach. Journal of the Operational Research Society 58, pages 1605–1611, 2007.

    Article  Google Scholar 

  44. Sparrow M.:. The application of netwrok analysis to criminal intelligence: an assessment of the prospects. Social Networks 13, pages 251–274, 1991.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automation and Control, Department of Electronic Systems, Aalborg University, Niels Bohrs Vej 8, 6700, Esbjerg, Denmark

    D. M. Akbar Hussain

Authors
  1. D. M. Akbar Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. M. Akbar Hussain .

Editor information

Editors and Affiliations

  1. Dept. of Comp. & Elect. Engin. and, Florida Atlantic University, Glades Road 777, Boca Raton, 33431, Florida, USA

    Borko Furht

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Hussain, D.M.A. (2010). Investigation of Key-Player Problem in Terrorist Networks Using Bayes Conditional Probability. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-7142-5_24

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-7141-8

  • Online ISBN: 978-1-4419-7142-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation