Uncloaking Terrorist Networks

By Valdis E. Krebs. First Monday, 2002

353 citations recorded by the source corpus.

This paper looks at mapping covert networks using data available from news sources on the World Wide Web. Specifically, we examine the network surrounding the tragic events of September 11th 2001. Through public data we are able to map a portion of the network centered on the 19 dead hijackers. This map gives us some insight into the terrorist organization, yet it is incomplete. Suggestions for further work and research are offered.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/122f87ca6e0baa97c4a8a428c5fb7b0b9ba1cac3

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Citations in the corpus

The efficiency/security trade-off in criminal networks (2007, background): “4 A slightly modified version of Krebs’ (2001) study was published about one year later (see Krebs, 2002).”
Identifying sets of key players in a social network (2006, background): “Fig. 7 Terrorist network compiled by Krebs (2002)”
Detecting critical nodes in sparse graphs (2009, background): “Furthermore, the CNP can be applied to the study of covert terrorist networks, where a certain number of individuals have to be identified whose deletion will result in the maximum breakdown of communication between individuals in the network [18].”
E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations (2003, methodology): “Sparrow [Sparrow, 1991] and Krebs [Krebs, 2002] both explore this concept, but to date there has not been (at least in the public domain) a treatment of a very large informal organization.”
Storage-class memory: The next storage system technology (2008, neutral): “networks [26]), image and video applications, and”
Quantitative Terrorism Risk Assessment (2002, background): “The sparse links between the nineteen hijackers have been charted by Krebs [2002], who has highlighted the way in which covert networks trade efficiency for secrecy, and the avoidance of detection.”
Extremal Properties of Random Structures (2003, neutral): “noting that “Euclidean Centrality is probably the closest to the reality” of the current criminal network analysis techniques. More recently, Krebs applied centrality measures and graphing techniques [28] to the terrorist networks uncovered in the 9/11 aftermath. He found that the average shortest p”