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

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


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.


Multi-partite networks Unsupervised learning Community detection Terrorism 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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