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September 2020 Identifying overlapping terrorist cells from the Noordin Top actor–event network
Saverio Ranciati, Veronica Vinciotti, Ernst C. Wit
Ann. Appl. Stat. 14(3): 1516-1534 (September 2020). DOI: 10.1214/20-AOAS1358

Abstract

Actor–event data are common in sociological settings, whereby one registers the pattern of attendance of a group of social actors to a number of events. We focus on 79 members of the Noordin Top terrorist network, who were monitored attending 45 events. The attendance or nonattendance of the terrorist to events defines the social fabric, such as group coherence and social communities. The aim of the analysis of such data is to learn about the affiliation structure. Actor–event data is often transformed to actor–actor data in order to be further analysed by network models, such as stochastic block models. This transformation and such analyses lead to a natural loss of information, particularly when one is interested in identifying, possibly overlapping, subgroups or communities of actors on the basis of their attendances to events. In this paper we propose an actor–event model for overlapping communities of terrorists which simplifies interpretation of the network. We propose a mixture model with overlapping clusters for the analysis of the binary actor–event network data, called $\mathtt{manet}$, and develop a Bayesian procedure for inference. After a simulation study, we show how this analysis of the terrorist network has clear interpretative advantages over the more traditional approaches of affiliation network analysis.

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Saverio Ranciati. Veronica Vinciotti. Ernst C. Wit. "Identifying overlapping terrorist cells from the Noordin Top actor–event network." Ann. Appl. Stat. 14 (3) 1516 - 1534, September 2020. https://doi.org/10.1214/20-AOAS1358

Information

Received: 1 August 2018; Revised: 1 August 2019; Published: September 2020
First available in Project Euclid: 18 September 2020

MathSciNet: MR4152144
Digital Object Identifier: 10.1214/20-AOAS1358

Keywords: Bayesian modeling , MCMC algorithm , Mixture models , network , overlapping clusters

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 3 • September 2020
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