Abstract
Social Network Analysis is a compilation of methods used to identify and analyze patterns in social network systems. This article serves as a primer on foundational social network concepts and analyses and builds a case study on the global Islamist terrorist network to illustrate the use and usefulness of these methods. The Islamist terrorist network is a system composed of multiple terrorist organizations that are socially connected and work toward the same goals. This research utilizes traditional social network, as well as small-world, and scale-free analyses to characterize this system on individual, network and systemic levels. Leaders in the network are identified based on their positions in the social network and the network structure is categorized. Finally, two vital nodes in the network are removed and this version of the network is compared with the previous version to make implications of strengths, weaknesses and vulnerabilities. The Islamist terrorist network structure is found to be a resilient and efficient structure, even with important social nodes removed. Implications for counterterrorism are given from the results of each analysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The term Islamist is used here and throughout the article to describe terrorists who operate on fundamental or radical Islamic ideologies, which often includes a literal and harsh interpretation of the Quran. The end goal of Islamist terrorism has been stated by some actors to be the establishment of the global caliphate.
This information is no longer available by the FMS Advanced Systems Group at www.trackingthethreat.com.
Degrees can be defined as consecutive connections through nodes in the network.
A geodesic path in a social network is the shortest path, counted by number of intermediate connections, between any two nodes.
A graph can be conceptualized here as a quantitative representation of a real network used for visualization and analysis.
Adjacent is used to describe closeness n social space and represents direct social connections.
Brokers in social networks can be conceptualized as those who are socially located between groups or individuals that have the potential to control flows (Burt, 1992).
Goodness of fit (R2) tests are used to determine how well the data fit a statistical model. In this case, the test measures the fit of the terrorist connection and frequency data against the linear log/log plot.
References
Armitage, R.L. (2002) Modification of description of ‘territory of Afghanistan controlled by the Taliban’ in executive order 13129, A notice by the State Department. The Federal Register 29 January, http://www.federalregister.gov/articles/2002/01/29/02-2244/modification-of-description-of-territory-of-afghanistan-controlled-by-the-taliban-in-executive-order.
Arquilla, J. and Ronfeldt, D. (2001) The advent of netwar (Revisited). In: J. Arquilla and D. Ronfeldt (eds.) Networks and Netwars: The Future of Terror, Crime, and Militancy: Rand Report MR-1382. Santa Monica, CA: Rand Corporation, pp. 1–25.
Baker, P., Cooper, H. and Mazzetti, M. (2011) Bin Laden is dead, Obama says. The New York Times 1 May, http://www.nytimes.com/2011/05/02/world/asia/osama-bin-laden-is-killed.html.
Bakier, A.H. (2010) A profile of al-Qaeda's new leader in Iraq: Abu Ayyub al-Masri. In: R. Mardini (ed.) Volatile Landscape: Iraq and Its Insurgent Movements. Washington DC: The Jamestown Foundation, pp. 112–114.
Barabási, A.-L. (2003) Linked: How Everything Is Connected to Everything Else and What It Means. New York: The Penguin Group.
Barabási, A.-L. and Albert, R. (1999) Emergence of scaling in random networks. Science 286 (5439): 509–512.
Bergen, P.L. (2001) Holy War Inc. New York: The Free Press.
Burt, R.S. (1992) Structural Holes: The Social Structure of Competition. Cambridge, MA. Harvard University Press.
CNN. (2010) Al Qaeda confirms death of 2 top leaders. CNN, 25 April, http://articles.cnn.com/2010-04-25/world/iraq.militant.leaders.killed_1_al-qaeda-abu-ayyub-al-masri?_s=PM:WORLD.
Erdős, P. and Rényi, A. (1959) On random graphs. Publications Mathematicae 6: 290–297.
FMS Advanced Systems Group. (2008) Network navigator, www.trackingthethreat.com, accessed 15 April 2008.
Freeman, L.C. (1979) Centrality in social networks: Conceptual clarification. Social Networks 1 (3): 215–239.
Goh, K-I., Oh, E., Jeong, H., Kahng, B. and Kim, D. (2002) Classification of scale-free networks. Proceedings of the National Academy of Sciences 99 (20): 12583–12588.
Grubesic, T. and Murray, A.T. (2006) Vital nodes, interconnected infrastructures, and the geographies of network survivability. Annals of the Association of American Geographers 96 (1): 64–83.
Gunaratna, R. (2002) Inside al Qaeda: Global Network of Terror. New York: The Berkley Publishing Group.
Gunaratna, R. (2004) The post-Madrid face of Al Qaeda. The Washington Quarterly 27 (3): 91–100.
Hanneman, R. and Riddle, M. (2005) Introduction to Social Network Methods. Riverside, CA: University of California.
International Centre for Political Violence and Terrorism Research (ICPVTR). (2008) Global Pathfinder Database, http://www.icpvtrdatabase.org/pls/icpvtr/InterQuest, accessed 20 June 2008.
Khalil, L. (2010) Evolving trends and insurgent groups. In: R. Mardini (ed.) Volatile Landscape: Iraq and Its Insurgent Movements. Washington DC: The Jamestown Foundation, pp. 36–39.
Kilcullen, D. (2005) Countering global insurgency. The Journal of Strategic Studies 28 (4): 597–617.
Koschade, S. (2006) A social network analysis of Jemaah Islamiyah: The applications to counterterrorism and intelligence. Studies in Conflict & Terrorism 29 (6): 559–575.
Krebs, V.E. (2002) Mapping networks of terrorist cells. Connections 24 (3): 43–52.
Lia, B. (2008) Architect of Global Jihad: The Life of al-Qaida Strategist Abu Mus’ab al-Suri. New York: Columbia University Press.
Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A. and Christakis, N. (2008) Tastes, ties, and time: A new social network dataset using facebook.com. Social Networks 30 (4): 330–342.
Magouirk, J., Atran, S. and Sageman, M. (2008) Connecting terrorist networks. Studies in Conflict & Terrorism 31 (1): 1–16.
Malm, A., Kinney, J.B. and Pollard, N.R. (2008) Social network and distance correlates of criminal associates involved in illicit drug production. Security Journal 21 (1): 77–94.
Malm, A., Bichler, G. and Walle, S.V.D. (2009) Comparing the ties that bind criminal networks: Is blood thicker than water? Security Journal 23 (1): 52–74.
Medina, R. and Hepner, G. (2008) Geospatial analysis of dynamic terrorist networks. In: I. Karawan, W. McCormack and S.E. Reynolds (eds.) Values and Violence: Intangible Aspects of Terrorism. Berlin: Springer, pp. 151–167.
Medina, R.M. and Hepner, G.F. (2011) Advancing the understanding of sociospatial dependencies in terrorist networks. Transactions in GIS 15 (5): 577–597.
Medina, R.M., Siebeneck, L.K. and Hepner, G.F. (2011) A geographic information systems (GIS) analysis of spatiotemporal patterns of terrorist incidents in Iraq 2004–2009. Studies in Conflict and Terrorism 34 (11): 862–882.
Milgram, S. (1967) The small-world problem. Psychology Today 1 (1): 61–67.
Mousseau, M. (2002/2003) Market civilization and its clash with terror. International Security 27 (3): 5–29.
Perry, C., McIntyre, J., Starr, B., Schuster, H. and Habib, R. (2006) Autopsy performed on al-Zarqawi: U.S. military: Cell phone helped track terrorist leader. CNN, 11 June, http://www.cnn.com/2006/WORLD/meast/06/10/iraq.al.zarqawi/index.html.
Ressler, S. (2006) Social network analysis as an approach to combat terrorism: Past, present, and future research. Homeland Security Affairs 2 (2).
Sageman, M. (2004) Understanding Terror Networks. Philadelphia, PA: University of Pennsylvania Press.
Sheridan, M.B. (2011) Zawahiri named new al-Qaeda leader. The Washington Post 16 June, http://www.washingtonpost.com/world/al-zawahiri-named-new-al-qaeda-leader/2011/06/16/AGNk87WH_story.html.
Sparrow, M.K. (1991) The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks 13 (3): 251–274.
Tsvetovat, M. and Carley, K.M. (2002) Knowing the enemy: A simulation of terrorist organizations and counter-terrorism strategies. Paper presented at the CASOS Conference; 23 June, Pittsburgh, PA.
U.S. Committee on Foreign Relations. (2010) Al Qaeda in Yemen and Somalia: A ticking time bomb. One Hundred Eleventh Congress, Second Session, 21 January, Washington DC, http://foreign.senate.gov/imo/media/doc/Yemen.pdf.
Wasserman, S. and Faust, K. (2007) Social Network Analysis: Methods and Applications. New York: Cambridge University Press.
Watts, D. and Strogatz, S.H. (1998) Collective dynamics of ‘small-world’ networks. Nature 393 (6684): 440–442.
Williams, M. (2010) Khalid Shaikh Mohammed. The New York Times, 29 January, http://topics.nytimes.com/topics/reference/timestopics/people/m/khalid_shaikh_mohammed/index.html.
Xu, J., Hu, D. and Chen, H. (2009) The dynamics of terrorist networks: Understanding the survival mechanisms of Global Salafi Jihad. Journal of Homeland Security and Emergency Management 6 (1): 1–15.
Author information
Authors and Affiliations
Appendix
Appendix
Formula to calculate network density:
where Δ is the network density, L is the number of observed links and g is the total number of nodes in the network. The denominator, g(g−1), gives the total number of links possible in a directed graph. L is multiplied by 2 in the case of an undirected graph (that is, a graph where links between nodes represent a connection and interactions are non-directional). A fully connected network will have a density of 1, while a network with no links will have a density of 0 (Wasserman and Faust, 2007).
The formula to calculate degree centrality:
where C d (n i ) is the degree for each node i and d(n i ) is the number of adjacent connections for node i Centrality values can standardized, as the interpretation of results may be dependent on the number of nodes in the network. Standardized results are reported on an index between 0 and The standardized degree centrality equation can be written as:
where C′ D (n i ) is the standardized degree centrality value for node i in the network, g is the number of nodes in the network and the denominator, g−1, represents all potential connections in the network for node i. Upon calculating the degree centrality for all nodes in a network, the values can be arranged, such that the list includes all network actors in order of their extent of social interaction based on their connections.
The formula to calculate the group degree centrality:
where C D (n*) is the largest observed centrality value and C D (n i ) is the degree centrality value for each node i. The denominator is the maximum possible of the sum of differences between the largest degree observed index value and the actor degree indices (Freeman, 1979; Wasserman and Faust, 2007). This metric reports network cenralization results between 0, which reflects a graph where all degrees are equal, such as a circle graph (that is, a graph conceptualized as nodes arranged in a circular formation and connected only to their neighbors), and 1, which reflects a graph completely centralized, such as a star graph (Wasserman and Faust, 2007).
The formula to calculate closeness centrality:
where C C (n i ) is the closeness centrality for node i and ∑g j=1 d(n i , n j ) is the sum of path distances by degree from node i to all other nodes j. In a standardized form, closeness can be written as:
where C′ C (n i ) the standardized closeness for node i, given by the inverse of the summation of the number of degrees in all geodesics from the target node to every other node in the network (shown in the denominator). The numerator in the equation, g−1, is the network size by number of nodes (g) minus one, which represents the potential connections of the target node (Wasserman and Faust, 2007).
The formula to calculate the closeness centrality for the entire network:
where C C is the group closeness centrality, C′ C (n*) is the maximum observed standardized closeness centrality, which is found by calculating the actor closeness centrality for all nodes, C′ C (n i ) is the standardized closeness centrality for each node i in the network, and the denominator represents the maximum possible value for the numerator and assures the results are between 0 and 1. Similar to degree centrality, 0 results in a network where the number of connections for each node is equal, and 1 results in a star graph where the network is completely centralized (Wasserman and Faust, 2007).
The formula to calculate actor betweenness centrality:
Where C B (n i ) is the betweenness centrality for node i, and ∑ j<k g jk (n i ) is the sum of all geodesics that transverse node i (Hanneman and Riddle, 2005). If multiple geodesics between two nodes and through varying nodes exist, the value of the geodesic is split, such that each between node takes its proportion of the value. For example, given three nodes that are all on separate but equal geodesic paths between a pair of nodes; each of the ‘between’ nodes is given 1/3 of the betweenness value, g jk (n i ) (Freeman, 1979).
This calculation can be normalized by the total number of geodesics in the network as:
where g jk is the total number of geodesics between node pairs. This metric can be further standardized to report results on an index from 0 to 1 in the form:
where C′ B (n i ) is the standardized betweenness centrality for node i and the maximum of C B (n i ) is given by (g−1)(g−2)/2.
The formula to calculate group betweenness centrality:
where C B is the group betweenness centrality, C′ B (n*) is the maximum observed standardized betweenness centrality and C′ B (n i ) is the standardized betweenness centrality for each node i in the network. In the range of results, a 0 occurs if all nodes have the same betweenness value and a 1 occurs in the case of a star graph (Wasserman and Faust, 2007).
The formula to calculate the characteristic path length, L(p):
where D g is the sum of degree values for all geodesics between node pairs, and N p is the total number of node pairings.
The formula to calculate the clustering coefficient, C(p):
where N c is the number of connections between neighbors, and N t is the total number of connections possible between individuals in a neighborhood. The target node is used only to define the neighborhood and is not included in the C(p) calculation (Watts and Strogatz, 1998).
Rights and permissions
About this article
Cite this article
Medina, R. Social Network Analysis: A case study of the Islamist terrorist network. Secur J 27, 97–121 (2014). https://doi.org/10.1057/sj.2012.21
Published:
Issue Date:
DOI: https://doi.org/10.1057/sj.2012.21