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MAPPing dark networks: A data transformation method to study clandestine organizations1

Published online by Cambridge University Press:  07 May 2014

LUKE M. GERDES
LUKE M. GERDES*
Affiliation:
Department of Behavioral Sciences & Leadership, United States Military Academy, West Point, NY 10996, USA (e-mail: luke.gerdes@usma.edu)
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Abstract

There is a growing consensus that dark networks require special methodological considerations, but little attention has been devoted to determining which data transformation processes are best suited to the study of dark networks. Standard approaches to the transformation of multi-modal data “fold” matrices, by binarizing the network and multiplying it against its transpose. Unfortunately, this process produces exaggerated results when applied to weighted networks, and consequently researchers often disregard information on tie-strength when transforming data. This paper evaluates previous attempts to overcome this limitation and assesses projection methods discovered by biologists and physicists, who have studied the problem of transforming weighted multi-modal networks within the specific context of these disciplines. However, the assumptions underlying these transformation processes limit their applicability to dark networks. This paper, therefore, offers the Median Additive Projection Process (MAPP), an approach to data transformation specifically designed for implementation in weighted, multi-modal dark networks. MAPP accounts for the ambiguity inherent to clandestine subject matter by treating relationship strength in a probabilistic fashion. Because agents' one-mode centrality rankings change significantly when different projection processes are applied to the same two-mode network, MAPP allows researchers to more accurately identify central actors in dark networks.

Keywords

dark networksdata-transformationprojectionmulti-modal networksweighted networks
Type
Research Article
Information
Network Science , Volume 2 , Issue 2 , August 2014 , pp. 213 - 253
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

1

This work was supported by the Office of the Secretary of Defense, Minerva Initiative. The views expressed herein are those of the author and do not purport to represent the official policy or position of the United States Military Academy, the Department of the Army, the Department of Defense, or the United States Government.

References

Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex networks. Proceedings of the National Academy of Sciences, 101 (11), 2747–3752; Retrieved from: http://arxiv.org/abs/cond-mat/0311416.CrossRefGoogle Scholar
Berlusconi, G. (2013). Do all the pieces matter? Assessing the reliability of law enforcement data sources for the network analysis of wire taps. Global Crime, 14 (1), 61–81.Google Scholar
Bolland, J. M. (1988). Sorting out centrality: An analysis of the performance of four centrality models in real and simulated networks. Social Networks, 10 (3), 233–253.Google Scholar
Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational and Mathematical Organizational Theory, 12, 21–34.Google Scholar
Borgatti, S. P. (2009). 2-Mode concepts in social network analysis. In Meyers, R. A. (Ed.), Encyclopedia of Complexity and Social Science (pp. 8279–8291). New York, NY: Springer.Google Scholar
Carrington, P. J. (2011). Crime and social network analysis. In Scott, J. & Carrington, P. J. (Eds.), SAGE Handbook of Social Network Analysis. Thousand Oaks, CA: SAGE Publications.Google Scholar
Csardi, G. & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.Google Scholar
Davis, A, Gardner, B. B., & Gardner, M. R. (1941). Deep South: A Social Anthropological Study of Caste and Class. Chicago, IL: University of Chicago Press.Google Scholar
Everton, S. F. (2012a). Network topography, key players, and terrorist networks. Connections, 32 (1), 12–19.Google Scholar
Everton, S. F. (2012b). Disrupting Dark Networks. New York, NY: Cambridge University Press.Google Scholar
Faust, K. (1997). Centrality in affiliation networks. Social Networks, 19, 157–191.Google Scholar
Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.Google Scholar
Krebs, V. E. (2002). Mapping networks of terrorist cells. Connections, 24 (3); Retrieved from: http://www.sfu.ca/~insna/Connections-Web/Volume24-3/Valdis.Krebs.web.pdf.Google Scholar
McCulloh, I. & Johnson, A. (2010). Advanced Network Analysis and Targeting Course Guide. Learning, Education, Analysis, Network Science (LEANS) LLC.Google Scholar
McDonald, J. H. (2009a). Spearman rank correlation. Handbook of Biological Statistics (2nd ed.). Baltimore, MD: Sparky House Publishing; Retrieved from: http://udel.edu/~mcdonald/statspearman.html.Google Scholar
McDonald, J. H. (2009b). Multiple comparison. Handbook of Biological Statistics (2nd ed.). Baltimore, MD: Sparky House Publishing; Retrieved from: http://udel.edu/~mcdonald/statmultcomp.html.Google Scholar
Morselli, C. (2009). Inside Criminal Networks (pp. 44–45). New York, NY: Springer.CrossRefGoogle Scholar
Newman, M. E. J. (2001). Scientific collaboration networks. II. Shortest Paths, weighted networks, and centrality. Physical Review E, 64, 016132.CrossRefGoogle ScholarPubMed
Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70, 056131.Google Scholar
Opsahl, T. (2009). Structure and Equivalence of Weighted Networks (pp. 104–122). London: UK: University of London, Queen Mary College; Retrieved from: http://toreopsahl.com/publications/thesis/ or http://toreopsahl.com/tnet/.Google Scholar
Opsahl, T. (2012). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 35, 159–167.CrossRefGoogle Scholar
Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest Paths. Social Networks, 32, 245–251.Google Scholar
Padrón, B., Nogales, M., & Traveset, A. (2011). Alternative approaches of transforming bimodal into unimodal mutualistic networks: The usefulness of preserving weighted information. Basic and Applied Ecology, 12, 713–721Google Scholar
Raab, J. & Milward, H. B. (2003). Dark networks as problems. Journal of Public Administration Research and Theory, 13 (4), 413–439.Google Scholar
Ramsey, P. H. (1989). Critical values for Spearman's rank order correlation. Journal of Educational Statistics, 14 (3), 243–253.Google Scholar
R Core Team (2012). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. Retrieved from: http://www.R-project.org/.Google Scholar
Roberts, N. & Everton, S. F. (2011). Strategies for combating dark networks. Journal of Social Structure, 12 (2).CrossRefGoogle Scholar
Sageman, M. (2004). Understanding Terror Networks. Philadelphia, PA: University of Pennsylvania Press.Google Scholar
Sageman, M. (2008). Leaderless Jihad: Terror Networks in the Twenty-First Century. Philadelphia, PA: University of Pennsylvania Press.Google Scholar
Sparrow, M. K. (1991). The application of network analysis to criminal intelligence: An assessment of the process. Social Networks, 13, 251–274.Google Scholar
Stephenson, K. & Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11 (1), 1–37.CrossRefGoogle Scholar
Tilly, C. (2005). Trust and Rule. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Tsvetovat, M. & Carley, K. M. (2005). Structural knowledge and success of anti-terrorism activity: The downside of structural equivalence. Journal of Social Structure, 6; Retrieved from: http://www.cmu.edu/joss/content/articles/volume6/TsvetovatCarley/.Google Scholar
Valente, T. W. (2010). Social Networks and Health: Models, Methods, and Applications (pp. 43–50). New York, NY: Oxford University Press.Google Scholar
Valente, T. W., Coronges, K., Lakon, C., & Costenbader, E. (2008). How correlated are network centrality measures? Connections, 28 (1), 16–26.Google Scholar
Xu, J. & Chen, H. (2008). The topology of dark networks. Communications of the ACM, 51 (10), 58–65.Google Scholar
Zhou, T., Ren, J., Medo, M., & Zang, Y. (2007). Bipartite network projection and personal recommendation. Physical Review E, 76, 046115.Google Scholar