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.

Detecting Latent Terrorist Communities Testing a Gower’s Similarity-Based Clustering Algorithm for Multi-partite Networks / Campedelli, Gian Maria; Cruickshank, Iain; Carley, Kathleen M.. - 812:(2019), pp. 292-303. ((Intervento presentato al convegno Complex Networks and Their Applications tenutosi a University of Cambridge nel December 2018 [10.1007/978-3-030-05411-3_24].

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

Campedelli, Gian Maria;
2019

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.
Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018
Cham
Springer
978-3-030-05410-6
978-3-030-05411-3
Campedelli, Gian Maria; Cruickshank, Iain; Carley, Kathleen M.
Detecting Latent Terrorist Communities Testing a Gower’s Similarity-Based Clustering Algorithm for Multi-partite Networks / Campedelli, Gian Maria; Cruickshank, Iain; Carley, Kathleen M.. - 812:(2019), pp. 292-303. ((Intervento presentato al convegno Complex Networks and Their Applications tenutosi a University of Cambridge nel December 2018 [10.1007/978-3-030-05411-3_24].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/289768
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