A COMPARATIVE SOCIAL NETWORK ANALYSIS OF THE 2008 MUMBAI, 2015 PARIS, and 2016 BRUSSELS TERRORIST NETWORKS

A COMPARATIVE SOCIAL NETWORK ANALYSIS OF THE 2008 MUMBAI, 2015 PARIS, and 2016 BRUSSELS TERRORIST NETWORKS

                                                                             ABSTRACT

This research builds a further understanding about analyses to characterize networks with limited

data available. It uses social network analysis to retrospectively compare the networks of the terrorist attacks in Mumbai 2008, Paris November 2015, and Brussels March 2016, to better recognize the roles and positions of the networks’ actors. Expanding on previous analysis of the Mumbai terrorist network, this paper identifies new methods to study dark networks by applying social network analysis to the Mumbai, Paris, and Brussels networks. Three levels of analysis are conducted: (1) an attribute-level correlation  to examine correlation between age and organizational role across cells; (2) key player analysis to investigate whether key players share similar roles; and (3) application of structural block models to the networks to identify cellular combat teams.

 

 

TABLE OF CONTENTS

List of Figures………………………………………………………………………………………………………… v

List of Tables…………………………………………………………………………………………………………. vi

Acknowledgments………………………………………………………………………… vii

Chapter 1 Introduction ………………………………………………………………………………………………. 1

Chapter 2 Events Orientation ……………………………………………………………………………………… 3

2.1 Mumbai, 2008 ……………………………………………………………………………………………… 3

2.2 Paris, 2015 …………………………………………………………………………………………………… 4

2.3 Brussels, 2016 ……………………………………………………………………………………………… 5

Chapter 3 Literature Review ………………………………………………………………………………………. 7

Chapter 4 Research Questions ……………………………………………………………………………………. 16

Chapter 5 Methodology …………………………………………………………………………………………….. 19

Chapter 6 Results ……………………………………………………………………………………………………… 21

6.1 Attribute-Level Correlation Analysis (RQ1) ……………………………………………………. 22

6.2 Key Player Analysis (RQ2) ……………………………………………………………………………. 24

6.2.1 Key Player Negative ……………………………………………………………………………. 24

6.2.2 Key Player Positive …………………………………………………………………………….. 28

6.3 Structural Blockmodeling (RQ3) ……………………………………………………………………. 30

Chapter 7 Discussion and Conclusion …………………………………………………………………………. 34

References ……………………………………………………………………………………………………………….. 37

 

 

LIST OF FIGURES

Figure 1-1: Number of Terror attacks globally since 1990.

Global Terrorism Database START (LaFree & Dugan, 2007) ………………………………………… 1

Figure 2-1: Target Locations in Mumbai (BBC News South Asia, 2008). ………………………… 4

Figure 2-2: Target Locations in Paris (BBC News Europe, 2015). ………………………………….. 5

Figure 2-3: Target Locations in Brussels (Wagner, 2016). ……………………………………………… 7

Figure 3-1: Number of Lone Wolf Attackers since 1950s (Worth, 2016) …………………………. 11

Figure 3-2: ISIS Attacks, Outside of its Self-Proclaimed Caliphate (Callimachi, 2017) ……… 11

Figure 3-3: Network with Central Node “1” that Does Not Fragment the Network (Borgatti, 2006) ….. 13

Figure 6-1: A Depiction of the Mumbai Terrorist Network (Borgatti et al., 2002) …………….. 21

Figure 6-2: A Depiction of the Paris Terrorist Network (Borgatti et al., 2002) …………………. 22

Figure 6-3: A Depiction of the Brussels Terrorist Network (Borgatti et al., 2002) …………….. 22

 

 

LIST OF TABLES

Table 6-1: Results for removal of one key player, based on fragmentation (Borgatti, S. P., 2003). ………………………………………………………………………………………………………………. 26

Table 6-2: Results for removal of two key players, based on fragmentation (Borgatti, S.

P., 2003). ………………………………………………………………………………………………………….. 27

Table 6-3: Results for monitoring of one key player, based on reach (Borgatti, S. P., 2003). ………………………………………………………………………………………………………………. 29

Table 6-4: Results for monitoring two key players, based on reach (Borgatti, S. P., 2003). … 30

Table 6-5: Block model output for the Mumbai network. Handler (H); Attacker (A). ……….. 31

Table 6-6: Block Model Output for Paris Network. Handler (H); Attacker (A). ………………… 32

Table 6-7: Block Model Output for Brussels Network. Handler (H); Operational Support (O); Attacker (A)……. 33

 

 

Chapter 1

Introduction

Gaining and in-depth understanding of how terrorist organizations and their respective cells are

structured and operate is a high priority for national security entities to combat terrorism in the post-9/11 world. According to the Global Terrorism Database, the number of terror attacks worldwide has increased dramatically since 1990 (see Figure 1-1) (LaFree & Dugan, 2007). Since 9/11, the U.S. has spent $1.6 trillion on the counterterrorism efforts in hopes to halt the upward trend (Belasco, 2009). Terror attacks have increased despite spending, which has raised the call for academic and practitioners alike to improve methods to understand terrorism and the social infrastructure that allows them to thrive. This paper works to advance this area by exploring social network analysis (SNA) techniques not commonly used to explore dark networks. It presents a comparative analysis of the social infrastructure involving in three different terror networks through three methodological techniques and describes their contribution to understanding networks. Such an understanding may help future initiatives to disrupt activities before violence occurs.

 

Figure 1-1: Number of Terror attacks globally since 1990.  Global Terrorism Database START (LaFree & Dugan, 2007)

SNA is a methodology that allows researchers to quantify and visualize social infrastructure, which creates unique opportunities to begin untangling the complexity that underlies terrorism. Although previous work has opened the door to the application of SNA to terrorist networks, Ressler (2006) contends that more work should be done to understand the causes of structuration within terrorist networks. This involves understanding:  How does a network structure itself? How is information spread throughout the network? And thus, how can a network be destabilized (Ressler, 2006)? The Mumbai,

Paris, and Brussels’ network structures, allowance for information diffusion, and vulnerability to be destabilized all point to the methodologies of this research.

The three cases of Mumbai, Paris, and Brussels were chosen for this analysis because of their similarity to one another in terms of operational structure, execution, and effectiveness. By leveraging existing data made available by Azad and Gupta (2011) in A quantitative assessment on 26/11 Mumbai attack using social network analysis, this research applies SNA techniques to the Mumbai terrorist network, while also building two additional networks around the 2015 Paris and 2016 Brussels attacks and offering a comparative analysis of the three networks. The overarching goals of this research are threefold: (1) offer empirically based network analysis to leverage insight towards counter-terrorism efforts; (2) better understand the structuration of contemporary terror networks; and (3) offer future direction for SNA-based terrorism research.  This study results in an evidenced-based approach to improved understanding of terror networks which should positively contribute to the development of counter-measures that disrupt and degrade activities and ultimately erode network’s effectiveness.

Building upon the progress made in applying social network analysis to terrorism, as well as utilizing previous insightful analyses conducted (i.e. comparative analyses), this study applies retrospective comparative analysis to advanced social network analysis techniques to evaluate terrorism and the related networks in a more comprehensive manner. By using social network analysis to aid in the understanding and operational tendencies of small dark networks, analysts will better visualize their findings and identify new and alternative hypotheses regarding future operations. The larger objectives of this paper are to explore commonalities that exist between the selected terror networks; explore common attributes among actors identified for removal; compare and contrast terror cell structuration. Based on these observations, we discuss comparative analysis as a tool for advancing terrorism related social network analysis research.

A COMPARATIVE SOCIAL NETWORK ANALYSIS OF THE 2008 MUMBAI, 2015 PARIS, and 2016 BRUSSELS TERRORIST NETWORKS

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