This post was written by Alis Wang
Academics at West Point’s Network Science Center recently published a paper introducing a new algorithm developed to help officials more effectively dismantle terrorist and insurgent networks. Their work focuses on how organizations regenerate their leadership cadres following an attack and argues that “shaping” networks to maximize network-wide centrality prior to targeting certain leaders can be more effective in disrupting a terrorist network than just focusing on removing “high value targets” or HVTs from the get-go. Currently, traditional Army targeting tends to focus on eliminating high-centrality nodes, such as leaders with many contacts within the network. However, due to the decentralized nature of many terrorist groups, these networks are able to quickly regenerate lost leadership and bounce back after a HVT is removed. For example, Al Qaeda leader Abu Musab al-Zarqawi was replaced a mere two weeks after he was killed in 2006. By instead “shaping”—or first decreasing the ability of a network to regenerate—prior to targeting HVTs, networks are then less able to recover after an attack.
The algorithm is relatively simple and looks at the “centrality” of a node within a network. For example, a node with greater centrality would have a higher number of ties to individuals within a certain network. The goal would be to target nodes that reduce the network’s ability to regenerate its leadership cadres. The authors believe that a more fragile network type is a “star-like” one, in which each node is only connected to a single, central node. Once this central node is taken out, it is much harder to replace. So the idea is to make a network look more “star-like” before targeting the HVT. The algorithm thus determines whether eliminating a certain node would make the overall network look more “star-like” or not.
The algorithm was tested against five different datasets with results showing that removing 12% of nodes can increase a network’s centrality by 17-45%, which means that taking out just a few mid-level individuals can make a network much more vulnerable. Testing against the datasets also indicated that existing Army techniques focusing on the removal of HVTs actually make a network less “star-like” and thereby stronger.
This theory of network degradation obviously has important implications for the counter-terrorism and counter-insurgency communities. A critical aspect of applying this theory would be network visualization and analysis. Intelligence analysis platforms, like Palantir, would be crucial tools that help analysts determine the current network pliability and figure out the most effective ways to shape the network for maximum degradation. By combining advanced analytic methodologies and innovative technological solutions, Praescient is helping its clients in their mission to disrupt and dismantle terrorist and insurgent networks.