Detecting latent terrorist communities testing a gower’s similarity-based clustering algorithm for multi-partite networks

Gian Maria Campedelli, Iain Cruickshank, Kathleen M. Carley

Risultato della ricerca: Contributo in libroContributo a convegno

2 Citazioni (Scopus)

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteComplex Networks and Their Applications VII
Pagine292-303
Numero di pagine12
Volume812
DOI
Stato di pubblicazionePubblicato - 2019
Evento7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 - Cambridge (UK)
Durata: 11 dic 201813 dic 2018

Serie di pubblicazioni

NomeSTUDIES IN COMPUTATIONAL INTELLIGENCE

Convegno

Convegno7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
CittàCambridge (UK)
Periodo11/12/1813/12/18

Keywords

  • Artificial Intelligence
  • Community detection
  • Multi-partite networks
  • Terrorism
  • Unsupervised learning

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