Abstract
Detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, but it remains a significant challenge to law enforcement. Framing our approach as a unique dynamic graph pattern matching problem, we address this challenge by introducing an analyst-in-the-loop framework and related technology called INSiGHT (Investigative Search for Graph-Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at developing tools for assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrate the validity of INSiGHT on two small synthetic datasets, and confirm that we can scale to and provide consistent results for a large, real world proxy dataset of over 470K nodes and 4 million edges.
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