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Analysis of world terror networks from the reduced Google matrix of Wikipedia

Author

Listed:
  • Samer El Zant

    (Institut de Recherche en Informatique de Toulouse, Université de Toulouse, INPT)

  • Klaus M. Frahm

    (Laboratoire de Physique Théorique du CNRS, IRSAMC, Université de Toulouse, CNRS, UPS)

  • Katia Jaffrès-Runser

    (Institut de Recherche en Informatique de Toulouse, Université de Toulouse, INPT)

  • Dima L. Shepelyansky

    (Laboratoire de Physique Théorique du CNRS, IRSAMC, Université de Toulouse, CNRS, UPS)

Abstract

We apply the reduced Google matrix method to analyze interactions between 95 terrorist groups and determine their relationships and influence on 64 world countries. This is done on the basis of the Google matrix of the English Wikipedia (2017) composed of 5 416 537 articles which accumulate a great part of global human knowledge. The reduced Google matrix takes into account the direct and hidden links between a selection of 159 nodes (articles) appearing due to all paths of a random surfer moving over the whole network. As a result we obtain the network structure of terrorist groups and their relations with selected countries including hidden indirect links. Using the sensitivity of PageRank to a weight variation of specific links we determine the geopolitical sensitivity and influence of specific terrorist groups on world countries. The world maps of the sensitivity of various countries to influence of specific terrorist groups are obtained. We argue that this approach can find useful application for more extensive and detailed data bases analysis.

Suggested Citation

  • Samer El Zant & Klaus M. Frahm & Katia Jaffrès-Runser & Dima L. Shepelyansky, 2018. "Analysis of world terror networks from the reduced Google matrix of Wikipedia," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(1), pages 1-11, January.
    • Handle: RePEc:spr:eurphb:v:91:y:2018:i:1:d:10.1140_epjb_e2017-80570-0
    DOI: 10.1140/epjb/e2017-80570-0
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    Statistical and Nonlinear Physics;

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