Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
STONE: shaping terrorist organizational network efficiency
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
PREVE: a policy recommendation engine based on vector equilibria applied to reducing LeT's attacks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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It has been known for some time that in terror networks, money laundering networks, and criminal networks, "important" players want to stay "off" the radar. They need sufficient centrality (according to traditional measures) to be well connected with the rest of their network, but need to blend in with the crowd. In this paper, we propose the concept of covertness centrality (CC). The covertness centrality of a vertex $v$ consists of two parts: how "common" $v$ is w.r.t. a set $\mathcal{C}$ of centrality measures, and how well $v$ can "communicate" with a user-specified set of vertices. The more "common" $v$ is, the more able it is to stay hidden in a crowd. Given $\mathcal{C}$, we first propose some general properties we would like a common-ness measure to satisfy. We then develop a probabilistic model of common-ness that a vertex has w.r.t. $\mathcal{C}$ (specifying, intuitively, how many other vertices are like it according to all centrality measures in $\mathcal{C}$). Covertness centrality of vertex $v$ is then defined as a linear combination of common-ness and the ability of $v$ to communicate with a user-specified set of other vertices. We develop a prototype implementation of CC and report on experiments we have conducted with it on several real-world data sets.