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Multimode co-clustering for analyzing terrorist networks

Author

Listed:
  • Ahmed Aleroud

    (Yarmouk University)

  • Aryya Gangopadhyay

    (University of Maryland, Baltimore County (UMBC))

Abstract

The phenomenon of terrorism is deemed one of the fundamental challenges in national security. Creating defensive technologies to mitigate terrorist attacks requires a simultaneous investigation of contextual relationships among their various dimensions. We proposed and evaluated a graph-based methodology to analyze terrorist networks through co-clustering in a multimode basis. Since there are many heterogeneous relationships in terrorist networks depending on the dimensions used during analysis, we utilized the clustering indicators of the multimode structure discovered in bi- and multimode graphs. Objects and activities that co-occur during terrorist attacks are identified by applying conventional clustering on those indicators. The novelty of our method is in the incremental creation of the multimode structure using its bi-mode counterparts. Our approach is evaluated using these measures: clustering stability and association confidence. The experimental results yields encouraging results in terms of simultaneous clustering of heterogeneous objects in terrorist networks.

Suggested Citation

  • Ahmed Aleroud & Aryya Gangopadhyay, 2018. "Multimode co-clustering for analyzing terrorist networks," Information Systems Frontiers, Springer, vol. 20(5), pages 1053-1074, October.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-016-9712-4
    DOI: 10.1007/s10796-016-9712-4
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    References listed on IDEAS

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    1. Marc Cheong & Vincent C. S. Lee, 2011. "A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter," Information Systems Frontiers, Springer, vol. 13(1), pages 45-59, March.
    2. Onook Oh & Manish Agrawal & H. Raghav Rao, 2011. "Information control and terrorism: Tracking the Mumbai terrorist attack through twitter," Information Systems Frontiers, Springer, vol. 13(1), pages 33-43, March.
    3. Jiexun Li & G. Alan Wang & Hsinchun Chen, 2011. "Identity matching using personal and social identity features," Information Systems Frontiers, Springer, vol. 13(1), pages 101-113, March.
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    Cited by:

    1. Raj Bridgelall, 2022. "Applying unsupervised machine learning to counterterrorism," Journal of Computational Social Science, Springer, vol. 5(2), pages 1099-1128, November.

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