Dark Web pp 91-103 | Cite as

Dark Network Analysis

  • Hsinchun ChenEmail author
Part of the Integrated Series in Information Systems book series (ISIS, volume 30)


Dark networks such as terrorist networks and narcotics-trafficking networks are hidden from our view yet could have a devastating impact on our society and economy. Understanding the topology of these dark networks can reveal greater insight into these clandestine organizations and help develop effective disruptive strategies. Based on analysis of four real-world “dark” networks, we found that these covert networks share many common topological properties with other types of networks. Their efficiency in communication and flow of information, commands, and goods can be tied to their small-world structures characterized by small average path length (l) and high clustering coefficient (C). In addition, we found that because of the small-world properties, dark networks are more vulnerable to attacks on the bridges that connect different communities than to attacks on the hubs. This may provide authorities with insight for intelligence and security purposes. An interesting finding about the three human dark networks is their substantially high clustering coefficients, which are not always present in other empirical networks.


Preferential Attachment Average Path Length Giant Component Crime Incident High Cluster Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Albert, R. and Barabási, A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics, 74 (1). 47–97, 2002.MathSciNetCrossRefzbMATHGoogle Scholar
  2. Barabási, A.-L., Jeong, H., Zéda, Z., Ravasz, E., Schubert, A. and Vicsek, T. Evolution of the social network of scientific collaborations. Physica A, 311. 590–614, 2002.MathSciNetCrossRefzbMATHGoogle Scholar
  3. Carlson, J.M. and Doyle, J. Highly optimized tolerance: A mechanism for power laws in designed systems. Physical Review E, 60 (2). 1412–1427, 1999.CrossRefGoogle Scholar
  4. Holme, P., Kim, B.J., Yoon, C.N. and Han, S.K. Attack vulnerability of complex networks. Physical Review E, 65. 056109, 2002.CrossRefGoogle Scholar
  5. Levitt, S.D. and Dubner, S.J. Freakonomics: A rogue economist explores the hidden side of everything. William Morrow, New York, NY, 2005.Google Scholar
  6. Liben-Nowell, D. and Kleinberg, J. The link prediction problem for social networks. in Proceedings of the 12th International Conference on Information and Knowledge Management, (New Orleans, LA, USA, 2003).Google Scholar
  7. Newman, M.E.J. Mixing patterns in networks. Physical Review E, 67 (2). 026126, 2003.MathSciNetCrossRefGoogle Scholar
  8. Sageman, M. Understanding Terror Networks. University of Pennsylvania Press, Philadelphia, PA, 2004.CrossRefGoogle Scholar
  9. Wasserman, S. and Faust, K. Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge, 1994.CrossRefzbMATHGoogle Scholar
  10. Watts, D.J. and Strogatz, S.H. Collective dynamics of “small-world” networks. Nature. 393. 440–442, 1998.CrossRefzbMATHGoogle Scholar
  11. Xu, J. and Chen, H. Untangling criminal networks: A case study. in Proceedings of the 1st NSF/NIJ Symposium on Intelligence and Security Informatics (ISI’03), (Tucson, AZ, 2003), 232–248.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Management Information SystemsUniversity of ArizonaTusconUSA

Personalised recommendations