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

  • Cassie McMillanEmail author
  • Diane Felmlee
  • Dave Braines
Original Paper



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.


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.


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.


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.


Terrorist networks Dynamic networks Transitivity Central hubs Efficiency Security 



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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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Sociology and CriminologyPennsylvania State UniversityUniversity ParkUSA
  2. 2.IBM ResearchWinchesterUK

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