A complex networks approach to find latent clusters of terrorist groups

Abstract Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a com...

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Main Authors: Gian Maria Campedelli, Iain Cruickshank, Kathleen M. Carley
Format: Article
Language:English
Published: SpringerOpen 2019-08-01
Series:Applied Network Science
Subjects:
Online Access:http://link.springer.com/article/10.1007/s41109-019-0184-6
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spelling doaj-31940dddebee482b827395ea893c020f2020-11-25T03:49:38ZengSpringerOpenApplied Network Science2364-82282019-08-014112210.1007/s41109-019-0184-6A complex networks approach to find latent clusters of terrorist groupsGian Maria Campedelli0Iain Cruickshank1Kathleen M. Carley2Transcrime - Università Cattolica del Sacro CuoreSchool of Computer Science - Carnegie Mellon UniversitySchool of Computer Science - Carnegie Mellon UniversityAbstract Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow to find latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose a novel algorithm for cluster formation that expands our earlier work that solely used Gower’s coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups’ ideologies. The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tend to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework.http://link.springer.com/article/10.1007/s41109-019-0184-6TerrorismPolitical violenceCommunity detectionComputational criminologyVon Neumann entropyGower’s coefficient
collection DOAJ
language English
format Article
sources DOAJ
author Gian Maria Campedelli
Iain Cruickshank
Kathleen M. Carley
spellingShingle Gian Maria Campedelli
Iain Cruickshank
Kathleen M. Carley
A complex networks approach to find latent clusters of terrorist groups
Applied Network Science
Terrorism
Political violence
Community detection
Computational criminology
Von Neumann entropy
Gower’s coefficient
author_facet Gian Maria Campedelli
Iain Cruickshank
Kathleen M. Carley
author_sort Gian Maria Campedelli
title A complex networks approach to find latent clusters of terrorist groups
title_short A complex networks approach to find latent clusters of terrorist groups
title_full A complex networks approach to find latent clusters of terrorist groups
title_fullStr A complex networks approach to find latent clusters of terrorist groups
title_full_unstemmed A complex networks approach to find latent clusters of terrorist groups
title_sort complex networks approach to find latent clusters of terrorist groups
publisher SpringerOpen
series Applied Network Science
issn 2364-8228
publishDate 2019-08-01
description Abstract Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow to find latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose a novel algorithm for cluster formation that expands our earlier work that solely used Gower’s coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups’ ideologies. The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tend to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework.
topic Terrorism
Political violence
Community detection
Computational criminology
Von Neumann entropy
Gower’s coefficient
url http://link.springer.com/article/10.1007/s41109-019-0184-6
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