Michael Ovelgonne
ABSTRACT
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.
- S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications. Cambrige: Cambridge University Press, 1994.Google Scholar
Cross Ref
- R. Lindelauf, P. Borm, and H. Hamers, "The influence of secrecy on the communication structure of covert networks," Social Networks, vol. 31, no. 2, pp. 126-137, 2009.Google ScholarCross Ref
- A. Gutfraind, "Optimizing topological cascade resilience based on the structure of terrorist networks," PLoS ONE, vol. 5, no. 11, 2010.Google Scholar
- W. W. Zachary, "An information flow model for conflict and fission in small groups," Journal of Anthropological Research, vol. 33, pp. 452-473, 1977.Google ScholarCross Ref
- U. Brandes, "On variants of shortest-path betweenness centrality and their generic computation," Social Networks, vol. 30, no. 2, pp. 136-145, 2008.Google Scholar
- D. S. Sade, "Sociometrics of macaca mulatta iii: n-path centrality in grooming networks," Social Networks, vol. 11, no. 3, pp. 273-292, 1989.Google ScholarCross Ref
- D. Gómez, E. González-Arangena, C. Manuel, G. Owen, M. del Pozo, and J. Tejada, "Centrality and power in social networks: a game theoretic approach," Mathematical Social Sciences, vol. 46, no. 1, pp. 27-54, 2003.Google Scholar
- R. Lindelauf, "Design and analysis of covert networks, affiliations and projects," Ph.D. dissertation, Tilburg School of Economics and management, 2011.Google Scholar
- S. P. Borgatti and M. G. Everett, "A graph-theoretic perspective on centrality," Social Networks, vol. 28, no. 4, pp. 466-484, 2006.Google ScholarCross Ref
- D. J. Watts and S. H. Strogatz, "Collective dynamics of small-world networks," Nature, vol. 393, no. 6684, pp. 440-442, 1998.Google Scholar
- C. Morselli, C. Gigure, and K. Petit, "The efficiency/security trade-off in criminal networks," Social Networks, vol. 29, no. 1, pp. 143-153, 2007.Google ScholarCross Ref
- K.-I. Goh, E. Oh, H. Jeong, B. Kahng, and D. Kim, "Classification of scale-free networks," Proceedings of the National Academy of Sciences, vol. 99, no. 20, pp. 12 583-12 588, 2002.Google ScholarCross Ref
- W. G. Madow and L. H. Madow, "On the theory of systematic sampling, i," The Annals of Mathematical Statistics, vol. 15, no. 1, pp. 1-24, 1944.Google ScholarCross Ref
- R. Jacob, D. Koschtzki, K. Lehmann, L. Peeters, and D. Tenfelde-Podehl, "Algorithms for centrality indices," in Network Analysis, U. Brandes and T. Erlebach, Eds. Springer Berlin/Heidelberg, 2005, vol. 3418, pp. 62-82.Google Scholar
- M. Berg, O. Cheong, M. Kreveld, and M. Overmars, Computational Geometry. Springer Berlin Heidelberg, 2008, ch. 5 - Orthogonal Range Searching, pp. 95-120.Google Scholar
- R. Guimerà, L. Danon, A. Dìaz-Guilera, F. Giralt, and A. Arenas, "Self-similar community structure in a network of human interactions," Physical Review E, vol. 68, p. 065103, Dec 2003.Google ScholarCross Ref
- A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, "Measurement and analysis of online social networks," Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, 2007. Google ScholarDigital Library
- M. Kendall, "A new measure of rank correlation," Biometrika, vol. 30, 1938.Google Scholar
- J. Baumes, M. Goldberg, M. Magdon-Ismail, and W. Wallace, "Discovering hidden groups in communication networks," in Intelligence and Security Informatics, H. Chen, R. Moore, D. Zeng, and J. Leavitt, Eds. Springer Berlin/Heidelberg, 2004, vol. 3073, pp. 378-389.Google Scholar
Recommendations
Diffusion Centrality in Social Networks
ASONAM '12: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)Though centrality of vertices in social networks has been extensively studied, all past efforts assume that centrality of a vertex solely depends on the structural properties of graphs. However, with the emergence of online "semantic" social networks ...
On Measurement of Influence in Social Networks
ASONAM '12: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)One of the issues to be resolved in social recommender systems is the identification of opinion leaders in a network. Finding effective people in societies has been a key question for many groups, e.g., marketers. The research undertaken in this paper ...
How Members of Covert Networks Conceal the Identities of Their Leaders
Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing ...
Comments