Covertness Centrality in Networks

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 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 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. C (specifying, intuitively, how many other vertices are like it according to all centrality measures in 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.