A Novel Graph Analytic Approach to Monitor Terrorist Networks
Kaustav Basu, Chenyang Zhou, Arunabha Sen, Victoria Horan Goliber

TL;DR
This paper introduces a new graph-based method for efficiently monitoring terrorist networks, enabling authorities to identify active suspects with fewer resources by analyzing their social connections.
Contribution
The paper presents a novel approach that reduces resource requirements for terror network monitoring while maintaining the ability to uniquely identify suspects in active planning.
Findings
Effective in real-world datasets
Reduces resource needs significantly
Maintains high identification accuracy
Abstract
Terrorist attacks all across the world have become a major source of concern for almost all national governments. The United States Department of State's Bureau of Counter-Terrorism, maintains a list of 66 terrorist organizations spanning the entire world. Actively monitoring a large number of organizations and their members, require considerable amounts of resources on the part of law enforcement agencies. Oftentimes, the law enforcement agencies do not have adequate resources to monitor these organizations and their members effectively. On multiple incidences of terrorist attacks in recent times across Europe, it has been observed that the perpetrators of the attack were in the suspect databases of the law enforcement authorities, but weren't under active surveillance at the time of the attack, due to resource limitations on the part of the authorities. As the suspect databases in…
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Taxonomy
TopicsComplex Network Analysis Techniques · Advanced Graph Neural Networks · Graph Theory and Algorithms
