Advertisement

The European Physical Journal Special Topics

, Volume 222, Issue 6, pp 1413–1439 | Cite as

The importance of centralities in dark network value chains

  • Noemi TothEmail author
  • László GulyásEmail author
  • Richard O. LegendiEmail author
  • Paul DuijnEmail author
  • Peter M. A. SlootEmail author
  • George KampisEmail author
Regular Article Applications in Different Domains

Abstract

This paper introduces three novel centrality measures based on the nodes’ role in the operation of a joint task, i.e., their position in a criminal network value chain. For this, we consider networks where nodes have attributes describing their “capabilities” or “colors”, i.e., the possible roles they may play in a value chain. A value chain here is understood as a series of tasks to be performed in a specific order, each requiring a specific capability. The first centrality notion measures how many value chain instances a given node participates in. The other two assess the costs of replacing a node in the value chain in case the given node is no longer available to perform the task. The first of them considers the direct distance (shortest path length) between the node in question and its nearest replacement, while the second evaluates the actual replacement process, assuming that preceding and following nodes in the network should each be able to find and contact the replacement. In this report, we demonstrate the properties of the new centrality measures using a few toy examples and compare them to classic centralities, such as betweenness, closeness and degree centrality. We also apply the new measures to randomly colored empirical networks. We find that the newly introduced centralities differ sufficiently from the classic measures, pointing towards different aspects of the network. Our results also pinpoint the difference between having a replacement node in the network and being able to find one. This is the reason why “introduction distance” often has a noticeable correlation with betweenness. Our studies show that projecting value chains over networks may significantly alter the nodes’ perceived importance. These insights might have important implications for the way law enforcement or intelligence agencies look at the effectiveness of dark network disruption strategies over time.

Keywords

European Physical Journal Special Topic Spearman Rank Correlation Centrality Measure Betweenness Centrality Color Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    H.B. Milward, J. Raab, Int. Public Manag. J. 9, 333 (2006)CrossRefGoogle Scholar
  2. 2.
    R.M. Bakker, J. Raab, H. Brinton Milward, J. Policy Anal. Manag. 31, 33 (2012)CrossRefGoogle Scholar
  3. 3.
    W.E. Baker, R.R. Faulkner, Amer. Sociol. Rev. 58, 837 (1993)CrossRefGoogle Scholar
  4. 4.
    T. Spapens, Global Crime 12, 19 (2011)CrossRefGoogle Scholar
  5. 5.
    C. Morselli, Inside Criminal Networks (New York: Springer, 2009)Google Scholar
  6. 6.
    V.E. Krebs, Connections 24, 43 (2001)Google Scholar
  7. 7.
    L. Ayling, Int. J. Law Crime Justice 37, 182 (2009)CrossRefGoogle Scholar
  8. 8.
    K.M. Carley, L. Ju-Sung, D. Krackhardt, Connections 24, 79 (2002)Google Scholar
  9. 9.
    M.A. Lauchs, R.L. Keast, D. Chamberlain, Crime, Law Social Change 57, 195 (2012)CrossRefGoogle Scholar
  10. 10.
    C. Morselli, C. Gigučre, K. Petit, Social Networks 29, 143 (2006)CrossRefGoogle Scholar
  11. 11.
    M. McPherson, Amer. Sociol. Rev., 519 (1983)Google Scholar
  12. 12.
    M. McPherson, Industrial Corporate Change 13, 263 (2004)CrossRefGoogle Scholar
  13. 13.
    J.M. McGloin, A.R. Piquero, J. Res. Crime Delinquency 47, 63 (2010)CrossRefGoogle Scholar
  14. 14.
    T. Spapens, Eur. J. Crime, Criminal Law Criminal Justice 18, 185 (2010)CrossRefGoogle Scholar
  15. 15.
    E.R. Kleemans, C.J. De Poot, Eur. J. Criminol. 5, 69 (2008)CrossRefGoogle Scholar
  16. 16.
    B. McCarthy, J. Hagan, L. Cohen, Social Forces 77, 155 (1998)Google Scholar
  17. 17.
    N. Coles, British J. Criminol. 41, 580 (2001)MathSciNetCrossRefGoogle Scholar
  18. 18.
    P. Gottschalk, Policing & Society 19, 47 (2009)CrossRefGoogle Scholar
  19. 19.
    R.S. Burt, in Social Capital: Theory and Research, edited by N. Lin, K.S. Cook & R.S. Burt (New Brunswick: Transaction Punblishers, 2001), p. 31Google Scholar
  20. 20.
    C. Morselli, Crime, Law Social Change 35, 203 (2001)CrossRefGoogle Scholar
  21. 21.
    P.M.A. Sloot, R. Quax, J. Comput. Sci. 9, 247 (2012)CrossRefGoogle Scholar
  22. 22.
    A. Czaplicka, J.A. Holyst, P.M.A. Sloot, Nature Scient. Reports 3 (2013)Google Scholar
  23. 23.
    S. Wasserman, K. Faust, Social Network Analysis – Methods and Applications (Cambridge University Press, 1994), p. 177Google Scholar
  24. 24.
    M.E.J. Newman, Networks – An Introduction (Oxford University Press, 2010), p. 168Google Scholar
  25. 25.
    A. Kalnins, V. Vitolins [arXivpreprintcs/0607044] (2006)
  26. 26.
    J. Qin, Th. Fahringer, S. Pllana, Distributed Parallel Systems 191 (2007)Google Scholar
  27. 27.
    T. Oinn, M. Addis, J. Ferris, D. Marvin, M. Senger, M. Greenwood, T. Carver, K. Glover, M.R. Pocock, A. Wipat, Bioinformatics 20, 3045 (2004)CrossRefGoogle Scholar
  28. 28.
    B. Ludäscher, I. Altintas, Ch. Berkley, D. Higgins, E. Jaeger, M. Jones, E.A. Lee, J. Tao, Y. Zhao, Concurr. Comput.: Practice Exper. 8, 1039 (2006)CrossRefGoogle Scholar
  29. 29.
  30. 30.
    W.M.P. van der Aalst, J. Circ. Syst., Comput. 8, 21 (1998)CrossRefGoogle Scholar
  31. 31.
    D. Georgakopoulos, M. Hornick, A. Sheth, Distributed Parallel Databases 3, 119 (1995)CrossRefGoogle Scholar
  32. 32.
    T.R. Jensen, B. Toft, Graph coloring problems (2011)Google Scholar
  33. 33.
    S.P. Borgatti, M.G. Everett, Social Networks 16, 43 (1994)MathSciNetCrossRefGoogle Scholar
  34. 34.
    F.T. Leighton, J. Res. National Bureau Standards 84, 489 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
  36. 36.
    M.E.J. Newman, Phys. Rev. E 74, 036104 (2006)MathSciNetADSCrossRefGoogle Scholar
  37. 37.
    M. Girvan, M.E.J. Newman, Proc. Natl. Acad. Sci. USA 99, 7821 (2002)MathSciNetADSCrossRefzbMATHGoogle Scholar
  38. 38.
    D. Lusseau, K. Schneider, O.J. Boisseau, P. Haase, E. Slooten, S.M. Dawson, Behav. Ecol. Sociobiol. 54, 396 (2003)CrossRefGoogle Scholar
  39. 39.
    D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  1. 1.Lorand Eötvös UniversityBudapestHungary
  2. 2.Aitia International, Inc.BudapestHungary
  3. 3.PetaByte Research Ltd.BudapestHungary
  4. 4.Research and AnalysisPolice Unit the HagueHagueThe Netherlands
  5. 5.University of AmsterdamAmsterdamThe Netherlands
  6. 6.Research Institute ITMOSt. PetersburgRussian Federation
  7. 7.Complexity ProgramNanyang Technological UniversitySingaporeSingapore
  8. 8.DFKI (German Research Insitute for Artificial Intelligence)KaiserslauternGermany

Personalised recommendations