Advertisement

Detecting Latent Terrorist Communities Testing a Gower’s Similarity-Based Clustering Algorithm for Multi-partite Networks

  • Gian Maria Campedelli
  • Iain Cruickshank
  • Kathleen M. Carley
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Finding hidden patterns represents a key task in terrorism research. In light of this, the present work seeks to test an innovative clustering algorithm designed for multi-partite networks to find communities of terrorist groups active worldwide from 1997 to 2016. This algorithm uses Gower’s coefficient of similarity as the similarity measure to cluster perpetrators. Data include information on weapons, tactics, targets, and active regions. We show how different dimensional weighting schemes lead to different types of grouping, and we therefore concentrate on the outcomes of the unweighted algorithm to highlight interesting patterns naturally emerging from the data. We highlight that groups belonging to different ideologies actually share very common behaviors. Finally, future work directions are discussed.

Keywords

Multi-partite networks Unsupervised learning Community detection Terrorism 

References

  1. 1.
    Belli, R., Freilich, J.D., Chermak, S.M., Boyd, K.A.: Exploring the crime-terror nexus in the United States: a social network analysis of a Hezbollah network involved in trade diversion. Dyn. Asymm. Confl. 8(3), 263–281 (2015).  https://doi.org/10.1080/17467586.2015.1104420Google Scholar
  2. 2.
    Benigni, M.C., Joseph, K., Carley, K.M.: Online extremism and the communities that sustain it: detecting the ISIS supporting community on Twitter. PLOS ONE 12(12), e0181,405 (2017).  https://doi.org/10.1371/journal.pone.0181405Google Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. (10), P10,008 (2008).  https://doi.org/10.1088/1742-5468/2008/10/P10008
  4. 4.
    Campedelli, G.M., Cruickshank, I., Carley, K.M.: Complex networks for terrorist target prediction. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) Social, Cultural, and Behavioral Modeling, vol. 10899, pp. 348–353. Springer International Publishing, Cham (2018)Google Scholar
  5. 5.
    Carley, K.M.: Destabilization of covert networks. Comput. Math. Org. Theory 12(1), 51–66 (2006).  https://doi.org/10.1007/s10588-006-7083-yGoogle Scholar
  6. 6.
    Chenoweth, E., Lowham, E.: On classifying terrorism: a potential contribution of cluster analysis for academics and policy-makers. Def. Sec. Anal. 23(4), 345–357 (2007).  https://doi.org/10.1080/14751790701752402Google Scholar
  7. 7.
    Desmarais, B.A., Cranmer, S.J.: Forecasting the locational dynamics of transnational terrorism: a network analytic approach. Sec. Inf. 2(1), 8 (2013).  https://doi.org/10.1186/2190-8532-2-8Google Scholar
  8. 8.
    Gower, J.C.: A general coefficient of similarity and some of its properties, 27Google Scholar
  9. 9.
    Kemp, C., Tenenbaum, J., Griffiths, T., Yamada, T., Ueda, : L.: Learning systems of concepts with an infinite relational model. AAAI 3 (2006)Google Scholar
  10. 10.
    Klausen, J.: Tweeting the Jihad : social media networks of western foreign fighters in Syria and Iraq. Stud. Confl. Terror. 38(1), 1–22 (2015).  https://doi.org/10.1080/1057610X.2014.974948Google Scholar
  11. 11.
    Koschade, S.: A social network analysis of jemaah islamiyah: the applications to counterterrorism and intelligence. Stud. Confl. Terror. 29(6), 559–575 (2006).  https://doi.org/10.1080/10576100600798418Google Scholar
  12. 12.
    Krebs, V.: Mapping networks of terrorist cells. Connections 24(3), 43–52 (2002)Google Scholar
  13. 13.
    LaFree, G., Dugan, L.: Introducing the global terrorism database. Terror. Polit. Viol. 19(2), 181–204 (2007).  https://doi.org/10.1080/09546550701246817Google Scholar
  14. 14.
    Lautenschlager, J., Ruvinsky, A., Warfield, I., Kettler, B.: Group profiling automation for crime and terrorism (GPACT). Proc. Manuf. 3, 3933–3940 (2015).  https://doi.org/10.1016/j.promfg.2015.07.922Google Scholar
  15. 15.
    von Luxburg, U.: A tutorial on spectral clustering (2007). arXiv:0711.0189 [cs], http://arxiv.org/abs/0711.0189
  16. 16.
    Medina, R., Hepner, G.: In: In: Karawan, I.A., McCormack, W., Reynolds, S.E. (eds.), Values and Violence. Geospatial Analysis of Dynamic Terrorist Networks, vol. 4, pp. 151–167. Springer, Netherlands, Dordrecht (2009).  https://doi.org/10.1007/978-1-4020-8660-1_10
  17. 17.
    Moon, I.C., Carley, K.M.: Modeling and simulating terrorist networks in social and geospatial dimensions. IEEE Intell. Syst. 22(5), 40–49 (2007).  https://doi.org/10.1109/MIS.2007.4338493Google Scholar
  18. 18.
    Qi, X., Christensen, K., Duval, R., Fuller, E., Spahiu, A., Wu, Q., Zhang, C.Q.: A hierarchical algorithm for clustering extremist web pages. In: 2010 International Conference on Advances in Social Networks Analysis and Mining, pp. 458–463 (2010).  https://doi.org/10.1109/ASONAM.2010.81
  19. 19.
    Ruan, J.: A fully automated method for discovering community structures in high dimensional data, pp. 968–973. IEEE (2009).  https://doi.org/10.1109/ICDM.2009.141
  20. 20.
    Sageman, M.: The stagnation in terrorism research. Terror. Polit. Viol. 26(4), 565–580 (2014). http://dx.doi.org/10.1080/09546553.2014.895649

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gian Maria Campedelli
    • 1
    • 2
  • Iain Cruickshank
    • 2
  • Kathleen M. Carley
    • 2
  1. 1.Università Cattolica del Sacro CuoreMilanItaly
  2. 2.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations