TY - GEN
T1 - Covertness centrality in networks
AU - Ovelgönne, Michael
AU - Kang, Chanhyun
AU - Sawant, Anshul
AU - Subrahmanian, V. S.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874271772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874271772&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2012.156
DO - 10.1109/ASONAM.2012.156
M3 - Conference contribution
AN - SCOPUS:84874271772
SN - 9780769547992
T3 - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
SP - 863
EP - 870
BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
T2 - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Y2 - 26 August 2012 through 29 August 2012
ER -
