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Dynamic Patterns of Terrorist Networks: Efficiency and Security in the Evolution of Eleven Islamic Extremist Attack Networks

Journal of Quantitative Criminology volume 36, pages 559–581 (2020)Cite this article

Abstract

Objectives

The current research examines how the efficiency/security tradeoff shapes the evolution of dynamic terrorist networks by focusing on the structural properties of these collectives. Some scholars argue that terrorist groups develop as chain-like, decentralized structures, while others maintain that terrorist networks form patterns of redundant ties and organize around a few highly connected individuals, or central hubs. We investigate these structural properties and consider whether patterns vary at different phases of a terrorist network’s formation.

Methods

Using a variety of descriptive network measures and Separable Temporal Exponential Random Graph Models, we consider patterns of tie formation across eleven multi-wave terrorism networks from the John Jay & ARTIS Transnational Terrorism database. This dataset includes networks from prominent attacks and bombings that occurred in the last 3 decades (e.g., the 2002 Bali Bombings), where nodes represent individual terrorists and ties represent social relationships.

Results

We find that terrorist groups navigate the efficiency/security tradeoff by developing increasingly well-connected networks as they prepare for a violent incident. Our results also show that highly central nodes acquire even more ties in the years directly preceding an attack, signifying that the evolution of terrorist networks tends to be structured around a few key actors.

Conclusions

Our findings have the potential to inform counterterrorism efforts by suggesting which actors in the network make the most influential targets for law enforcement. We discuss how these strategies should vary as extremist networks evolve over time.

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Notes

  1. Additionally, according to Gerdes (2015), it is occasionally difficult to correctly distinguishing between the various relationship types in the JJATT data.

  2. For instance, in the network that surrounded the November 17th attacks of 2000, all ties were present during each wave of interest. Because there was no observable change, this network was excluded from our final sample.

  3. While these attack networks are distinct, they are not independent. Our sample includes individual terrorists who participated in multiple attack networks.

  4. It is important to note that we cannot verify whether the relational ties observed during the attack year occurred before or after the group orchestrated the attack of interest.

  5. For two of our networks (the 2000 Philippines Ambassador residence bombing and the 2000 Christmas Eve bombing), there are limited data on the structure of the network before each attack took place. As a result, our analyses of these two networks is characterized by larger gaps of time (e.g., 5 years) between the earliest waves of data.

  6. In order for our multivariate models to achieve satisfactory goodness of fit statistics, it is necessary that we observe a time period with a sufficient degree of tie formation and tie dissolution. Across our five waves of data, the vast majority of tie formation (99.9%) occurred between Wave 1 and Wave 4, and only 9.8% of observed tie dissolution transpired between these time points. As a result, we include Wave 5 in our analytic sample because it introduces a necessary degree of tie dissolution for our statistical models to converge.

  7. Note that Helfstein and Wright (2011) argue that it is unlikely that terrorist network structure and officials’ willingness to collect and release relational data are associated and, thus, this limitation is unlikely to substantively bias analyses of structural processes.

  8. Transitivity is the geometrically weighted edgewise shared parameter and brokerage is the geometrically weighted dyadwise shared parameter (see Snijders et al. 2006). For all terms that require a decay parameter, we use a weight of 0.10.

  9. In supplemental STERGMs, we include node match formation parameters to account for organizational role homophily and education homophily among the individual terrorists in our sample (available upon request). Including these controls does not substantively alter the interpretation of our parameters of interest. However, small sample sizes and missing data on these individual-level characteristics prevent us from examining these controls further.

  10. We assess the goodness of fit of our STERGMs by comparing the degree distribution, distribution of edge-wise shared partners, and the distribution of geodesic distances of the observed networks to the simulated networks generated by the STERGMs (analyses available upon request). The STERGMs presented here come close to matching the observed data on all three of these higher-order statistics.

  11. Degree, closeness, and betweenness centralization reach their maximum value of 1 in star-shaped graphs where one actor is tied to all other actors and these actors are not tied to each other. The measures reach their minimum score of 0 in networks where all actors share equal actor indices for the centralization measure of interest (Wasserman and Faust 1994).

  12. It is important to note that the lack of power in these supplemental analyses preclude us from relying on their findings. The number of terrorist networks in our sample is too limited to justify a thorough analysis of contextual variation. We leave further questions about variation across terrorist networks open to future research.

  13. Note that these findings only apply to those networks included in our final analytic sample. Due to the constraints of our modeling approach, it was necessary to exclude attack networks that did not exhibit any structural change over the five waves of interest.

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Acknowledgements

This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. This work was also supported by Pennsylvania State University and the National Science Foundation under an IGERT award # DGE-1144860, Big Data Social Science.

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Authors and Affiliations

  1. Department of Sociology and Criminology, Pennsylvania State University, 211 Oswald Tower, University Park, PA, 16801, USA

    Cassie McMillan & Diane Felmlee

  2. IBM Research, Hursley Park, Winchester, SO21 2JN, UK

    Dave Braines

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  1. Cassie McMillan
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Correspondence to Cassie McMillan.

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McMillan, C., Felmlee, D. & Braines, D. Dynamic Patterns of Terrorist Networks: Efficiency and Security in the Evolution of Eleven Islamic Extremist Attack Networks. J Quant Criminol 36, 559–581 (2020). https://doi.org/10.1007/s10940-019-09426-9

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Keywords

  • Terrorist networks
  • Dynamic networks
  • Transitivity
  • Central hubs
  • Efficiency
  • Security