Emerging Trends in The Social and Behavioral Sciences

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

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Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

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Emerging Trends in Social Network
Analysis of Terrorism and
Counterterrorism
DAVID KNOKE

Abstract
A key issue in tracking transnational terror trends is the utility of social network
analysis, both as a theoretical perspective and as a methodological toolkit, for understanding and assessing terror organizations, and for developing counterterror policies and practices to detect and disrupt terror attacks. Foundational efforts were
case studies of particular groups or operations, culling data from newspaper reports
and court trial documents, then creating matrix files for analysis with social network computer programs. Mathematicians, game theorists, and computer scientists
are dramatically expanding research beyond foundational case studies of terrorist
networks. Much of their work centers on devising strategies for counterterror organizations to destabilize clandestine organizations. They develop elegant and precise
mathematical models and computer algorithms, then systematically change parameters to assess capabilities of detecting and disrupting terrorist activities under varying conditions. Key issues for future network research include: conducting rigorous
comparative analyses of four historical waves of modern terrorism for clues about
the present and future waves; building more comprehensive, cohesive, and integrated theoretical models capable of explaining the formation, structure, and consequences of terrorist networks; developing new methods of measuring network
relations among terrorists; performing more laboratory experiments as an alternative
to collecting inaccessible and dangerous field observation data; and creating large,
high-quality relational datasets to test social network theories of terrorism.

INTRODUCTION
Although terrorism recurs throughout human history, the recent wave of
transnational jihadism arose over the past four decades and shows few signs
of abating. The social structures and dynamics of both terrorist networks
and counterterror organizations coevolved and continue their mutual
adaptations to changing environments and innovative technologies. A key
issue in tracking transnational terror trends is the utility of social network
analysis, both as a theoretical perspective and as a methodological toolkit,
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for understanding and assessing terror organizations, and for developing
counterterror policies and practices to detect and disrupt terror attacks.
Advances in terrorism research have implications for investigating and
thwarting other types of criminal clandestine, covert, and dark networks,
such as arms-trafficking, diamond smuggling, human and sexual trafficking,
nuclear proliferation, toxic waste disposal, and trade in endangered species.
These applications have broader significance for the many professions and
fields contributing to terrorism studies, including sociology, criminology,
organization studies, political science, international relations, military
science, mathematics, computer science, security studies, law, and law
enforcement.
The myriad definitions of terrorism typically emphasize the political
objectives of individuals or groups engaged in violent acts. These goals
often derive from an ideology, such as anarchism or Marxism, or from
group identification, such as nationalist or religious affiliation. This article
concentrates on emerging trends in transnational terror networks and
counterterror efforts against them. Title 22 of the United States Code
defines international terrorism as “premeditated, politically motivated
violence perpetrated against non-combatant targets by subnational groups
or clandestine agents” which involves citizens or the territory of more than
one country (U.S. Department of State, 2013, p. 293). Further, section 219
of the United States’ Immigration and Nationality Act defines a “foreign
terrorist organization” (FTO) as one that “must engage in terrorist activity
… or terrorism … or retain the capability and intent to engage in terrorist
activity or terrorism” and “must threaten the security of U.S. nationals or
the national security (national defense, foreign relations, or the economic
interests) of the United States” (p. 244). For 2012, the State Department
designated 51 FTOs fitting this definition. On the basis of their thumbnail
sketches, 36 FTOs were Islamist; three Colombian; two Irish; two Greek; and
one ach Basque, Filipino, Israeli, Japanese, Kurdish, Peruvian, Sri Lankan,
and Turkish. Of course, many hundreds of terrorist groups operate within
national or subnational territories.
FOUNDATIONAL RESEARCH
Although Claire Sterling’s The Terror Network (1981) described links among
Palestinian and Irish Republican Army terrorists and the KBG in the 1970s,
academic researchers began applying social network methods to terrorist
networks after the 9/11 Al-Qaida attacks inside the United States. Foundational efforts were case studies of particular groups or operations, culling
data from newspaper reports and court trial documents, then creating matrix
files for analysis with social network computer programs. Within weeks

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after 9/11, Valdis Krebs (2001, 2002) constructed a seminal two-dimensional
diagram (graph) showing the connections among the 19 hijackers and 43
accomplices who provided money, skills, and information. Mohammed
Atta, leader of the field operations, was the central actor and the four pilots
formed a tight clique. Every hijacker was two or fewer steps from two
others, Nawaf Alhazmi and Khalid Almihdhar. The Central Intelligence
Agency knew these two men had participated in a January 2000 planning
meeting with top Al-Qaida leaders in Malaysia. But, the CIA failed to inform
the Federal Bureau of Investigation when the men returned to America
(National Commission, 2004). The Intelligence Community’s failure to track
these suspects was a missed opportunity to detect and possibly apprehend
the 9/11 network before it struck. Krebs (2001) concluded that a “dense
under-layer of prior trusted relationships made the hijacker network both
stealth and resilient,” but “concentrating both unique skills and connectivity
in the same [persons] makes the network easier to disrupt—once it is
discovered.” In another foundational analysis, Marc Sageman (2004) used
biographies of 366 participants in the “global Salafi network” to identify ties
based on kinship, friendship, religious, and work relations. He found four
large clusters built around highly connected “hubs”: Al-Qaida Central Staff,
Maghreb Arabs (North Africa), Core Arabs (Saudi Arabia, Egypt, Yemen,
Kuwait), and Southeast Asians (Indonesia and Malaysia). However, his
only network diagram was schematic, rather than based on actual relations
among specific individuals.
Other network researchers constructed matrices and diagrams of known
connections within terrorist cells. Steven Koschade (2006) found a high density of ties (0.43) among 17 members of a large Jemaah Islamiyah cell that
bombed a Bali, Indonesia, nightclub in 2002. The structure was very centralized, revealing a “mix between efficiency and covertness” (p. 570). Field
commander Samudra and logistics commander Idris had exceptionally high
scores on three centrality measures, which placed both men at the center of
the graph. Samudra had “the highest ability to access others, and the greatest
control over the flow of information in the network . . . . Exclusively due to his
connection to Team Lima [support group], and the suicide bomber Arnasan”
(p. 571). Given the high connectivity inside the cell, detection and capture
of any member by law enforcement might have exposed the entire group
and thwarted the bombing. By comparing four terrorist cells operating in or
against Australia, Koschade (2007) concluded that cells focused on efficiency
rather than covertness were more successful in carrying out attacks. Betweenness centrality (control over information flow) was crucial for identifying cell
leaders. A broader analysis of Jemaah Islamiyah mapped a half-dozen leadership, kinship, and attack networks from 1993 to 2005 (Magouirk, Atran, &
Sageman, 2008).

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A network of 45 jihadis conducted the March 11, 2004, Madrid train bombings (Jordán, Mañas, & Horsburgh, 2008). Although that operation was
inspired by Osama bin Laden’s explicit threat to target Spain because of its
military involvement in the Iraq War, the participants had few connections
to Al-Qaida. Instead, it was a largely grassroots action organized by three
immigrants of Moroccan and Tunisian origins. Its decentralized cell structure had advantages of “autonomy of operational and tactical command
and control, the capacity to adapt to environments, logistical autonomy, and
protection by way of judicial guarantees” (p. 35). However, lack of professionalism and reliance on open social networks for recruitment, financing,
and arms and explosives made the Madrid operation potentially vulnerable
to detection and disruption. Ami Pedahzur and Arie Perliger (2006) diagrammed four Palestinian networks behind 42 suicide bombings during the
al-Aqsa Intifada and Jewish terrorist networks inside Israel that targeted
Palestinians and assassinated Prime Minister Yitzak Rabin (Pedahzur &
Perliger, 2009). Other researchers applied network analytic methods to the
Turkish Ergenekon terrorist organization (Demiroz & Kapucu, 2012), the
Bakri-Hamza network at London’s Finsbury Park mosque (Horne & Horgan,
2012), a global network of 381 persons affiliated with Islamist organizations
(Medina, 2014), and a Melbourne jihadi cell disrupted before it could launch
an attack (Harris-Hogan, 2013). Common features of these analyses were
formal network measures that identify central, inner circle, and peripheral
actors and plot their positions and connections in diagrams.
A related strand of terrorist research asserted an emerging nexus between
organized criminals and terrorist groups. Terrorists not only engage in
criminal activities to fund operations, but many transact with organized
criminals to buy and sell goods and services, such as weapons and forged
documents. Tamara Makarenko (2005) saw the origins of the nexus in 1980s
Latin American narco-terrorism, exemplified in drug trafficking by the Revolutionary Armed Forces of Colombia (FARC). The terrorism-crime nexus
went global in the 1990s with the ascendancy of transnational organized
crime. Some analysts saw increasing integration (Curtis & Karacan, 2002),
but others doubted whether genuine convergence is feasible (Dishman,
2001; Dandurand & Chin, 2004). This unresolved debate offers opportunities
for future research.
Theorists speculated that counterterror pressures by law enforcement and
military forces compel terrorist groups to change their structures and actions.
In seeking an optimal balance between resilient security and communication
inefficiencies, hierarchical organizations become more decentralized. Brian
Jackson (2006) proposed an evolutionary process from tightly coupled
organization, to coupled network, and thence to loosely coupled movement. Loose-coupling hinders the ability of counterterror organizations

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to detect tactical cells and “what actions should be taken to identify and
exploit any vulnerabilities found there” (p. 242). Al-Qaida exemplified
this trajectory from a corporate-like command structure before 9/11 to a
subsequent “leaderless jihad” (Sageman, 2008). But, as decentralized cells
acquire more independence to manage their logistics and select their own
targets, top leaders lose control and efficiency. The shift to weakly connected
network structures risks imperiling the organization’s core mission, when
incompetent rogue cells launch unsuccessful and counterproductive attacks.
CUTTING-EDGE RESEARCH
Mathematicians, game theorists, and computer scientists are dramatically
expanding research beyond foundational case studies of terrorist networks.
Much of their work centers on devising strategies for counterterror organizations to destabilize clandestine organizations. They develop elegant and
precise mathematical models and computer algorithms, then systematically
change parameters to assess capabilities of detecting and disrupting terrorist
activities under varying conditions. Some models are not based on empirical data, but are theory-driven efforts that simulate network dynamics with
artificial datasets. Other models harvest vast quantities of information from
open sources, such as news reports and Websites, then mine these texts for
data patterns that identify key network actors, relations, and properties. This
section discusses a few exemplary cutting-edge models of both kinds.
An early effort applied the mathematical theory of ordered sets to quantify
the extent to which a terrorist group ceases to function when some members
are captured or killed (Farley, 2003). Assuming a hierarchical cell structure of
leaders and followers, the model enables counterterror agencies to estimate
the probability of disconnecting a network by removing a specified number of members. The method involves searching for the network’s cutset, the
network actors whose removal breaks all vertical chains of command linking leaders to foot soldiers. Of course, the mathematical model is moot for
real terrorist groups that are not structured as hierarchical communication
networks. (For related work, see Farley, 2007, 2009 and McGough, 2009.)
Another approach applied graph theoretic metrics to recognize and understand network structural properties. Lindelauf, Borm, and Hamers (2009a)
compared models of covert communication networks to find structures with
optimal trade-offs between two group objectives: secrecy to avoid detection
and operational efficiency of information flow to coordinate and control
cell members. Which model is optimal depends on assumptions about the
likely exposure of all cell members if one person is randomly detected.
For example, a star graph (all cell members communicate only with the
leader) is the optimal structure for balancing the conflicting objectives if the

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detection of one member also exposes all his links to the other cell members.
In contrast, if the probability of exposure varies by member centrality in the
network, the optimal structures are reinforced rings and reinforced wheel
graphs. Other scenarios making different exposure assumptions and imbalanced secrecy-efficiency trade-offs identify different optimal structures.
(For related models, see Lindelauf, Borm, & Hamers, 2009b, 2010.) Notably
absent from these graph theoretic models are counterterror organizations
that actively try to detect and disrupt the terror networks.
Computational methods allow computer simulations of terrorist networks
and the impacts of alternative counterterror strategies on their resiliency
and capacity to conduct future attacks. Agent-based modeling methods
involve “(i) the simulation of automated agent behaviors and interactions
in the context of their environments; (ii) the analysis of macro-level patterns resulting from micro-level agent interactions” (Keller, Desouza, &
Lin, 2010, p. 1020). By running thousands of simulations under varying
parameter assumptions, researchers can provide some understanding and
insight into potentially effective counterterror strategies against terrorist
networks adapting to their opponents’ actions. An example is the Stochastic
Opponent Modelling Agent (SOMA) package of computational and network tools that used textual data automatically extracted from document
sources to generate rules explaining a terrorist group’s behavior (Sliva,
Subrahmanian, Martinez, & Simari, 2008). Applied to 25 years of monthly
data on the Pakistan-based Lashkar-e-Taiba (LeT), SOMA learned 10 rules
that predicted when LeT was most likely to attack targets in the disputed
Jammu and Kashmir provinces of India and Pakistan (Mannes, Shakarian, &
Subrahmanian, 2011). These rules could “provide accurate probabilistic forecasts for both real and hypothetical situations,” helping policymakers and
counterterror organizations make strategic decisions (p. 6). Other exemplary
data-mining, event-forecasting, link-prediction models, and experimental
methods include Mahesh, Mahesh, and Vinayababu (2010); Arce, Croson,
and Eckel, (2011); Chaurasia, Dhakar, Tiwari, and Gupta (2012); Desmarais
and Cranmer (2013); and Petroff, Bond, and Bond (2013).
Kathleen Carley’s (2003) Dynamic Network Analysis (DNA) package integrated traditional social network analysis of actor-to-actor links with computational multiagent modeling to connect actors, locations, events, tasks,
knowledge, resources, and other elements. It treats terrorist groups as “complex dynamic networked systems that evolve over time” (Carley, 2006, p. 1).
DNA is a “toolchain” of computer programs for collecting extracting data
from texts, mapping networks of words in texts, and forecasting changes.
“Map analysis systematically extracts and analyzes the links between words
in a text in order to model the author’s ‘mental map’ as networks of words”
(Diesner & Carley, 2004, p. 2). Network analytic methods identify actors’

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spheres of influence, emergent leaders, and paths among critical actors. Theories of social interaction, such as homophily, can be applied to estimate the
probability of a tie between two actors where no connection is observed.
Analysts can run can virtual DNA experiments, simulating the removal of
actors and observing the consequences. Quantitative measures assess potential immediate and near-term threats from alternative actions by counterterror organizations. Carley (2006) illustrated DNA with an automatic collection of thousands of open-source texts about Iraq and Al-Qaida. The emerging network had a “decidedly cellular structure with 5–12 persons per cell”
(p. 5). Over a decade, it decreased in density and communication levels, but
increased in congruence, suggesting “a movement to a more distributed and
efficient organizational form, possibly with larger cells” (p. 4). One counterterror implication was that removing highly central actors in communication
network would be less effective than taking out key emergent leaders. DNA
software was provided to the Counter-Terror Social Network Analysis and
Intent Recognition (CT-SNAIR) project, which sought to develop automated
tools to “connect the dots” in raw multimedia data by modeling and simulating terrorist networks and attacks (Weinstein, Campbell, Delaney, & O’Leary,
2009, p. 1). A serious limitation was the difficulty in obtaining “truth-marked
data” (p. 13) to test the SNAIR algorithms.
KEY ISSUES FOR FUTURE RESEARCH
Constantly mutating transnational terror networks will shape emerging
trends in the social network analysis of terrorism and counterterrorism.
Although predicting the actions of terrorist groups is notoriously imprecise,
some broad tendencies are discernible. Under pressures by counterterror
organizations, global jihadism evolved during the past two decades from
centralized hierarchies to networked groups, then to fragmented or isolated
cells. Disconnected units are more difficult to detect and disrupt, especially
lone-wolf attacks (Borum, Fein, & Vossekuil, 2012), such as the November 9,
2009, Fort Hood shooting and the April 15, 2013, Boston Marathon bombing.
Unstable and failed states increasingly offer sanctuaries for terrorists to
assemble, train, plan, and launch operations, such as the September 21, 2013,
attack by Al-Shabaab gunmen from Somalia on Westgate Mall in Nairobi,
Kenya. Insurgencies and guerilla wars, flaring across Libya, Mali, Yemen,
Sudan, the Sinai, Syria, and other parts of the Middle East and North Africa,
offer training grounds for terrorist organizations and their foot soldiers to
acquire arms, weapon skills, and combat experience. With the impending
2014 withdrawal of U.S. and NATO forces from Afghanistan, the Taliban,
Al-Qaida, and “a dozen like-minded groups … are slowly and steadily
returning to Afghanistan, re-creating the pre-9/11 sanctuary” (Gunaratna,

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2013, p. 2). Although the U.S. and its allies decimated the original Al-Qaida
top leadership, in the past five years, proliferating affiliate groups “have
unquestionably expanded their operational reach and capability … We can
be certain, however, that they will have much more extensive resources and
capabilities than any group that has yet tried to attack us, if and when they
do” (Kagan, 2013, pp. 5–6). Transnational terror will likely plague the planet
into the foreseeable future.
Trends in transnational counterterrorism will likely continue the brutal,
lethal, and sometimes illegal government strategies and tactics that emerged
after 9/11 (Kurtulus, 2011). The Bush Administration’s prosecution of “the
global war against terror” violated the Geneva Conventions on torture and
human rights, greatly expanded the national security state, and harmed
domestic civil liberties (Chadwick, 2003; Liese, 2009). The Obama Administration curbed some egregious abuses of power, for example, closing
the CIA’s secret “black site” prisons and banning coercive interrogation
methods. But, it extended other Bush counterterror policies and practices,
such as asserting the state secrets privilege and federal immunity from
lawsuits on behalf of tortured victims of U.S. renditions (Cole, 2010).
Targeted killings of suspected terrorists by drone strikes in Afghanistan,
Pakistan, and Yemen increased fivefold, with high numbers of civilian
casualties, euphemistically called collateral damage. Obama greatly expanded
the National Security Agency’s phone and Internet surveillance programs
(Greenwald, 2013). By creating a chain of contacts from massive metadata
mining, NSA seeks “to identify hubs or common contacts between targets
of interest who were previously thought to be unconnected, and potentially
to discover individuals willing to become US Government assets” (Lizza,
2013, p. 57). After former NSA contractor Edward Snowden revealed the
vast scope of data-dredging, one federal judge ruled the program an unconstitutional violation of privacy: “No court has ever recognized a special need
sufficient to justify continuous, daily searches of virtually every American
citizen without any particularized suspicion” (Nakashima & Marimow,
2013). But, another judge ruled it legal. Whatever the ultimate outcome of
judicial appeals, presidential reviews, and congressional restrictions, cyber
snooping will undoubtedly remain a counterterror priority for detecting
dubious data.
Scholars in the interdisciplinary field of terrorism studies too often trail
behind event-driven trends in transnational terrorism. To get ahead of the
curve, researchers must look beyond investigating contemporary incidents
to understanding broader contexts and longer-range perspectives. Some key
issues and opportunities for future network research include:
Conduct rigorous comparative analyses of four historical waves of modern
terrorism for clues about the present and future waves. An anarchist wave,

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beginning in late-19th century, was followed by anti-colonialist, New Left,
and the contemporary jihadist wave (Rapoport, 2001). Comparing each
wave’s long-term network dynamics will yield important contrasts and
insights into their similar and unique trajectories. One result will be better
understanding of the origins of terror campaigns, responses of counterterror
organizations, innovation and evolution of strategies and tactics, and
processes of desistance that bring terrorism to an end.
Build more comprehensive, cohesive, and integrated theoretical models capable of explaining the formation, structure, and consequences of
terrorist networks. Analytic models of network dynamics must explicate
the interpersonal processes by which people are recruited to clandestine organizations, trained in nefarious skills, allocated to organizational
positions, and assigned roles in terror operations. Elements for building
social network theories of terrorism will be drawn from diverse social
science disciplines, encompassing psychological, sociology, geographic,
political, economic, and related paradigms. Connecting these elements
necessitates close collaborations among substantive experts. Generating
testable hypotheses will benefit from the participation of researchers from
the computational sciences. Barriers to effective interdisciplinary research
must be overcome, particularly the lack of understanding of alternative
professional perspectives and incompatible taken-for-grant assumptions.
Develop new methods of measuring network relations among terrorists. In
addition to improving the accuracy of automated text analysis techniques,
how will more reliable information be extracted from photographic, video,
and audio recordings? Will security software, such as biometric authentication and face-recognition software, be adapted to generate new network
data? How will these diverse modes of data collection be effectively integrated using network analytic methods?
Perform more laboratory experiments as an alternative to collecting
inaccessible and dangerous field observation data. Researchers will
construct theoretically based models of interdependent terrorist and counterterror networks comprising both computer programs and human subjects.
Controlled manipulation of parameters, such as information and costs, will
test hypotheses predicting actor reactions and network structural changes.
Investigators will study the impacts of varying scenarios on subjects’ actions
and collective outcomes such as detection, deterrence, disruption, network
resilience, security decision, resource allocation, target selection, and attack
success. For greater complexity and realism, experimental findings will be
adapted to massively multiuser online role-playing games pitting virtual
terrorists against counterterror agents.
Create large, high-quality relational datasets to test social network theories of terrorism. Researchers will shift from case studies of particular

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events to encompassing systems of people, organizations, institutions, and
events. Counterterror actions will be integrated with terrorist behaviors
to create more realistic coevolving network dynamics. Given the paucity
of primary data collected from terrorists, many researchers will continue
to depend on collecting secondary data from public documents. Other
analysts will emphasize the importance of the Internet and cyberspace
communication networks linking thousands of extremist Websites for propaganda, radicalization, recruitment, and financial transactions. Vastly more
sophisticated massive data-mining algorithms will improve content-based
pattern detection. But, quality assurance will necessitate such automated
routines be supplemented by painstaking hands-on correction of gaps and
errors.
Regardless of specific future directions, social network researchers must
surely rise to the challenge of how to use network analytic theory and
methods for better understanding, detecting, and thwarting of miscreants
engaged in terrorist activities.
REFERENCES
Arce, D. G., Croson, R. T. A., & Eckel, C. C. (2011). Terrorism experiments. Journal of
Peace Research, 48, 373–382. doi:10.1177/0022343310391502
Borum, R., Fein, R., & Vossekuil, B. (2012). A dimensional approach to analyzing
lone offender terrorism. Aggression and Violent Behavior, 17, 389–396. doi:10.1016/
j.avb.2012.04.003
Carley, K. M. (2003). Dynamic network analysis. In R. Breiger, K. Carley & P. Pattison
(Eds.), Dynamic social network modeling and analysis: Workshop summary and papers
(pp. 133–145). Washington, DC: Committee on Human Factors, National Research
Council.
Carley, K. M. (2006). A dynamic network approach to the assessment of terrorist groups and the impact of alternative courses of action. In Visualising Network Information. Meeting Proceedings RTO-MP-IST-063, Keynote 1 pp.
KN1-1–KN1-10. Neuilly-sur-Seine, France: RTO. Retrieved from http://www.
vistg.net/documents/IST063_PreProceedings.pdf.
Chadwick, E. (2003). It’s war, Jim, but not as we know it: A ‘reality-check’ for international laws of war? Crime, Law and Social Change, 39, 233–262. doi:10.1023/
A:1022907030010
Chaurasia, N., Dhakar, M., Tiwari, A., & Gupta, R. K. (2012). A survey on terrorist network mining: Current trends and opportunities. International Journal of
Computer Science & Engineering Survey, 3(4) Retrieved from http://www.airccse.
org/journal/ijcses/papers/3412ijcses05.pdf. doi:10.5121/ipcses.2012.3405
Cole, D. (2010). Breaking away. New Republic December 30. Retrieved from
http://www.newrepublic.com/article/magazine/politics/79752/breakingaway-obama-bush-aclu-guantanamo-war-on-terror.

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

11

Curtis, G., & Karacan, T. (2002). The Nexus among terrorists, narcotics traffickers, weapons proliferators, and organized crime networks in Western Europe. Washington, DC: Federal Research Division, Library of Congress. Retrieved from
http://www.loc.gov/rr/frd/pdf-files/WestEurope_NEXUS.pdf.
Dandurand, Y., & Chin, V. (2004). Links between terrorism and other forms of crime.
Vancouver, BC: International Centre for Criminal Law Reform and Criminal
Justice Policy. Retrieved from http://icclr.law.ubc.ca/sites/icclr.law.ubc.ca/files/
publications/pdfs/LinksBetweenTerrorismLatest_updated.pdf.
Demiroz, F., & Kapucu, N. (2012). Anatomy of a dark network: the case of the
Turkish Ergenekon terrorist organization. Trends in Organized Crime, 15, 271–295.
doi:10.1007/s12117-012-9151-7
Desmarais, B. A., & Cranmer, S. J. (2013). Forecasting the locational dynamics
of transnational terrorism: A network analytic approach. Security Informatics, 2,
1–13. Retrieved from http://www.security-informatics.com/content/pdf/21908532-2-8.pdf. doi:10.1186/2190-8532-2-8
Diesner, J., & Carley, K. M. (2004). Using network text analysis to detect the organizational structure of covert networks (Retrieved from http://citeseerx.ist.psu.edu/
viewdoc/download?doi=10.1.1.206.5240&rep=rep1&type=pdf).
Dishman, C. (2001). Terrorism, crime, and transformation. Studies in Conflict & Terrorism, 24, 43–58. doi:10.1080/10576100118878
Farley, J. D. (2003). Breaking Al Qaeda cells: A mathematical analysis of counterterrorism operations (A guide for risk assessment and decision making). Studies in
Conflict & Terrorism, 26, 399–411. doi:10.1080/10576100390242857
Farley, J. D. (2007). Toward a mathematical theory of counterterrorism: Building the perfect terrorist cell. Proteus Monograph Series Volume 1 Issue 2. Retrieved from
http://www.rit.edu/∼w-cmmc/literature/Proteus.pdf.
Farley, J. D. (2009). Two theoretical research questions concerning the structure of
the perfect terrorist cell. In N. Memon, J. D. Farley, D. L. Hicks & T. Rosenorn
(Eds.), Mathematical Methods in Counterterrorism (pp. 91–103). New York, NY:
Springer-Verlag/Wien.
Greenwald, G. (2013). NSA collecting phone records of millions of Verizon customers daily. The Guardian June 5. Retrieved from www.guardian.co.uk/world/
2013/jun/06/nsa-phone-records-verizon-court-order.
Gunaratna, R. (2013). Terrorists to bounce back in 2013. Counter Terrorist Trends and
Analysis, 5, 2–4.
Harris-Hogan, S. (2013). Australian neo-jihadist terrorism: Mapping the network and
cell analysis using wiretap evidence. Studies in Conflict & Terrorism, 35, 298–314.
doi:10.1080/1057610X.2012.656344
Horne, C., & Horgan, J. (2012). Methodological triangulation in the analysis of terrorist networks. Studies in Conflict & Terrorism, 35, 182–192. doi:10.1080/1057610X.
2012.639064
Jackson, B. A. (2006). Groups, networks, or movements: A command-and-controldriven approach to classifying terrorist organizations and its application to Al
Qaeda. Studies in Conflict & Terrorism, 29, 241–262. doi:10.1080/10576100600564042

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Jordán, J., Mañas, F. M., & Horsburgh, N. (2008). Strengths and weaknesses of grassroot jihadist networks: The Madrid bombings. Studies in Conflict & Terrorism, 31,
17–39. doi:10.1080/10576100701767148
Kagan, F. W. (2013). The continued expansion of al Qaeda affiliates and their
capabilities. Washington: Testimony before the House Committee on Foreign
Affairs Subcommittee on Terrorism, Nonproliferation, and Trade. Retrieved from
http://www.aei.org/speech/foreign-and-defense-policy/terrorism/al-qaeda/
the-continued-expansion-of-al-qaeda-affiliates-and-their-capabilities.
Keller, J., Desouza, K. C., & Lin, Y. (2010). Dismantling terrorist networks: Evaluating strategic options using agent-based modeling. Technological Forecasting & Social
Change, 77, 1014–1036. doi:10.1016/j.techfore.2010.02.007
Koschade, S. (2006). A social network analysis of Jemaah Islamiyah: The applications
to counterterrorism and intelligence. Studies in Conflict & Terrorism, 29, 559–575.
doi:10.1080/10576100600798418
Koschade, S. (2007). The internal dynamics of terrorist cells: A social network analysis of terrorist cells in an Australian context. Unpublished doctoral dissertation, Queensland University of Technology, Brisbane, Australia. Retrieved from
http://eprints.qut.edu.au/16591.
Krebs, V. (2001). Mapping networks of terrorist cells. Connections, 24, 43–52. Retrieved
from http://vlado.fmf.uni-lj.si/pub/networks/doc/Seminar/Krebs.pdf.
Krebs, V. (2002). Uncloaking terrorist networks. First Monday, 7, 1–14. Retrieved from
http://firstmonday.org/ojs/index.php/fm/article/view/941.
Kurtulus, E. N. (2011). The new counterterrorism: Contemporary counterterrorism
trends in the United States and Israel. Studies in Conflict & Terrorism, 35, 37–58.
doi:10.1080/1057610X.2012.631456
Liese, A. (2009). Exceptional necessity: How liberal democracies contest the prohibition of torture and ill-treatment when countering terrorism. Journal of International
Law and International Relations, 5, 17–47.
Lindelauf, R., Borm, P., & Hamers, H. (2009a). The influence of secrecy on the communication structure of covert networks. Social Networks, 31, 126–137. doi:10.1016/
j.socnet.2008.12.003
Lindelauf, R., Borm, P., & Hamers, H. (2009b). On heterogeneous covert networks.
In N. Memon, J. D. Farley, D. L. Hicks & T. Rosenorn (Eds.), Mathematical methods
in counterterrorism (pp. 215–228). New York, NY: Springer-Verlag/Wien.
Lindelauf, R., Borm, P., & Hamers, H. (2010). One-mode projection analysis and
design of covert affiliation networks. Tilburg, Netherlands: Tilburg University Center for Economic Research, Discussion Paper 2010–53. Retrieved from
https://pure.uvt.nl/portal/files/1225410/2010-53.pdf.
Lizza, R. (2013). State of deception: Why won’t the president rein in the intelligence
community? New Yorker, December, 16, 48–61.
Magouirk, J., Atran, S., & Sageman, M. (2008). Connecting terrorist networks. Studies
in Conflict & Terrorism, 31, 1–16. doi:10.1080/10576100701759988
Mahesh, S., Mahesh, T. R., & Vinayababu, M. (2010). Using data mining techniques
for detecting terror-related activities on the Web. Journal of Theoretical & Applied
Information Technology, 16, 99–104.

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

13

Makarenko, T. (2005). Terrorism and transnational organized crime: Tracing the
crime-terror nexus in South East Asia. In P. Smith (Ed.), Terrorism and violence in
South East Asia: Transnational challenges to states and regional stability (pp. 169–187).
New York, NY: M. E. Sharpe.
Mannes, A., Shakarian, J., & Subrahmanian, V. S. (2011). A computationally-enabled
analysis of Lashkar-e-Taiba attacks in Jammu and Kashmir. In Intelligence and
Security Informatics Conference (EISIC), 2011 European (pp. 224–229. Retrieved
from http://shakarian.net/janaPapers/let_eisic_camera.pdf. doi:10.1109/EISIC.
2011.61).
McGough, L. R. (2009). Mathematically modeling terrorist cells: Examining the
strength of structures of small sizes. In N. Memon, J. D. Farley, D. L. Hicks & T.
Rosenorn (Eds.), Mathematical methods in counterterrorism (pp. 55–67). New York,
NY: Springer-Verlag/Wien.
Medina, R. M. (2014). Social network analysis: A case study of the Islamist terrorist
network. Security Journal, 27, 97–121. Retrieved from. doi:10.1057/sj.2012.21
Nakashima, E., & Marimow, A. E. (2013). Judge: NSA’s collecting of phone records is
probably unconstitutional. Washington Post December 16. Retrieved from http://
www.washingtonpost.com/national/judge-nsas-collecting-of-phone-records-islikely-unconstitutional/2013/12/16/6e098eda-6688-11e3-a0b9-249bbb34602c_
story.html.
National Commission on Terrorist Attacks Upon the United States (2004). The
9/11 commission report. New York, NY: W.W. Norton. Retrieved from http://
www.gpo.gov/fdsys/pkg/GPO-911REPORT/pdf/GPO-911REPORT.pdf.
Pedahzur, A., & Perliger, A. (2006). The changing nature of suicide attacks: A social
network perspective. Social Forces, 84, 1987–2008. doi:10.1353/sof.2006.0104
Pedahzur, A., & Perliger, A. (2009). Jewish terrorism in Israel. New York, NY: Columbia
University Press.
Petroff, V. B., Bond, J. H., & Bond, D. H. (2013). Using hidden Markov models to predict terror before it hits (again). In V. S. Subrahmanian (Ed.), Handbook of computational approaches to counterterrorism (pp. 163–180). New York, NY:
Springer-Verlag/Wien.
Rapoport, D. C. (2001). The fourth wave: September 11 in the history of world terrorism. Current History, 100, 419–424.
Sageman, M. (2004). Understanding terror networks. Philadelphia: University of Pennsylvania Press.
Sageman, M. (2008). Leaderless jihad: Terror networks in the twenty-first century.
Philadelphia: University of Pennsylvania Press.
Sliva, A., Subrahmanian, V. S., Martinez, V., & Simari, G. I. (2008). The SOMA Terror
Organization Portal (STOP): Social network and analytic tools for the real-time
analysis of terror groups. First International Workshop on Social Computing,
Behavioral Modeling, and Prediction. Retrieved from http://content.schweitzeronline.de/static/content/catalog/newbooks/978/038/777/9780387776712/
9780387776712_Excerpt_001.pdf.
Sterling, C. (1981). The terror network: The secret war of international terrorism. New
York, NY: Holt, Rinehart and Winston.

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U.S. Department of State (2013). Country reports on terrorism 2012. Retrieved from
http://www.state.gov/j/ct/rls/crt/2012.
Weinstein, C., Campbell, W., Delaney, B., & O’Leary, G. (2009). Modeling and detection techniques for counter-terror social network analysis and intent recognition.
Institute of Electrical and Electronics Engineers. Retrieved from dspace.mit.edu/
openaccess-disseminate/1721.1/71803.

FURTHER READING
Everton, S. F. (2012). Disrupting dark networks. New York, NY: Cambridge University
Press.
McCulloh, I., Armstrong, H., & Johnson, A. (2013). Social network analysis with applications. New York, NY: John Wiley & Sons, Inc.
Memon, N., Farley, J. D., Hicks, D. L., & Rosenorn, T. (Eds.) (2009). Mathematical methods in counterterrorism. New York, NY: Springer-Verlag/Wien.
Mullins, S. (Ed.) (2013). Special issue: Applying social network analysis to terrorism.
Behavioral Sciences of Terrorism & Political Aggression, 5(2), 67–175.
Ranstorp, M. (Ed.) (2007). Mapping terrorism research: State of the art, gaps and future
direction. New York, NY: Routledge.
Subrahmanian, V. S. (Ed.) (2013). Handbook of computational approaches to counterterrorism. New York, NY: Springer-Verlag/Wien.

DAVID KNOKE SHORT BIOGRAPHY
David Knoke is professor of sociology at the University of Minnesota, where
he teaches courses in statistics, networks, and organizations. He received his
PhD in 1972 from the University of Michigan and was professor of sociology
at Indiana University from 1972 to 1985. He was a Fulbright research scholar
at Kiel University (1989) and a fellow at the Center for Advanced Study in the
Behavioral Sciences (1992). In 2008, he received the University of Minnesota
College of Liberal Arts’ Arthur “Red” Motley Exemplary Teaching Award.
With various colleagues, he received several National Science Foundation
research grant and published the results in research monographs on political, organizational, and social network behavior. Some of these books are The
Organizational State, Organizing for Collective Action, Political Networks, Organizations in America, Comparing Policy Networks, Changing Organizations, Social
Network Analysis, and Economic Networks. His current research investigates
diverse social networks, including intra- and interorganizational, health care,
economic, financial, terrorist, and counterterror networks.
RELATED ESSAYS
Problems Attract Problems: A Network Perspective on Mental Disorders
(Psychology), Angélique Cramer and Denny Borsboom

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

15

Migrant Networks (Sociology), Filiz Garip and Asad L. Asad
Interdependence, Development, and Interstate Conflict (Political Science),
Erik Gartzke
Herd Behavior (Psychology), Tatsuya Kameda and Reid Hastie
Regime Type and Terrorist Attacks (Political Science), Kara Kingma et al.
How Networks Form: Homophily, Opportunity, and Balance (Sociology),
Kevin Lewis
Network Research Experiments (Methods), Allen L. Linton and Betsy Sinclair
Culture, Diffusion, and Networks in Social Animals (Anthropology), Janet
Mann and Lisa Singh
Gender and Women’s Influence in Public Settings (Political Science), Tali
Mendelberg et al.
The Role of School-Related Peers and Social Networks in Human Development (Psychology), Chandra Muller
Social Relationships and Health in Older Adulthood (Psychology), Theodore
F. Robles and Josephine A. Menkin
How Do Labor Market Networks Work? (Sociology), Brian Rubineau and
Roberto M. Fernandez
War and Social Movements (Political Science), Sidney Tarrow
Creativity in Teams (Psychology), Leigh L. Thompson and Elizabeth Ruth
Wilson



Emerging Trends in Social Network
Analysis of Terrorism and
Counterterrorism
DAVID KNOKE

Abstract
A key issue in tracking transnational terror trends is the utility of social network
analysis, both as a theoretical perspective and as a methodological toolkit, for understanding and assessing terror organizations, and for developing counterterror policies and practices to detect and disrupt terror attacks. Foundational efforts were
case studies of particular groups or operations, culling data from newspaper reports
and court trial documents, then creating matrix files for analysis with social network computer programs. Mathematicians, game theorists, and computer scientists
are dramatically expanding research beyond foundational case studies of terrorist
networks. Much of their work centers on devising strategies for counterterror organizations to destabilize clandestine organizations. They develop elegant and precise
mathematical models and computer algorithms, then systematically change parameters to assess capabilities of detecting and disrupting terrorist activities under varying conditions. Key issues for future network research include: conducting rigorous
comparative analyses of four historical waves of modern terrorism for clues about
the present and future waves; building more comprehensive, cohesive, and integrated theoretical models capable of explaining the formation, structure, and consequences of terrorist networks; developing new methods of measuring network
relations among terrorists; performing more laboratory experiments as an alternative
to collecting inaccessible and dangerous field observation data; and creating large,
high-quality relational datasets to test social network theories of terrorism.

INTRODUCTION
Although terrorism recurs throughout human history, the recent wave of
transnational jihadism arose over the past four decades and shows few signs
of abating. The social structures and dynamics of both terrorist networks
and counterterror organizations coevolved and continue their mutual
adaptations to changing environments and innovative technologies. A key
issue in tracking transnational terror trends is the utility of social network
analysis, both as a theoretical perspective and as a methodological toolkit,
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for understanding and assessing terror organizations, and for developing
counterterror policies and practices to detect and disrupt terror attacks.
Advances in terrorism research have implications for investigating and
thwarting other types of criminal clandestine, covert, and dark networks,
such as arms-trafficking, diamond smuggling, human and sexual trafficking,
nuclear proliferation, toxic waste disposal, and trade in endangered species.
These applications have broader significance for the many professions and
fields contributing to terrorism studies, including sociology, criminology,
organization studies, political science, international relations, military
science, mathematics, computer science, security studies, law, and law
enforcement.
The myriad definitions of terrorism typically emphasize the political
objectives of individuals or groups engaged in violent acts. These goals
often derive from an ideology, such as anarchism or Marxism, or from
group identification, such as nationalist or religious affiliation. This article
concentrates on emerging trends in transnational terror networks and
counterterror efforts against them. Title 22 of the United States Code
defines international terrorism as “premeditated, politically motivated
violence perpetrated against non-combatant targets by subnational groups
or clandestine agents” which involves citizens or the territory of more than
one country (U.S. Department of State, 2013, p. 293). Further, section 219
of the United States’ Immigration and Nationality Act defines a “foreign
terrorist organization” (FTO) as one that “must engage in terrorist activity
… or terrorism … or retain the capability and intent to engage in terrorist
activity or terrorism” and “must threaten the security of U.S. nationals or
the national security (national defense, foreign relations, or the economic
interests) of the United States” (p. 244). For 2012, the State Department
designated 51 FTOs fitting this definition. On the basis of their thumbnail
sketches, 36 FTOs were Islamist; three Colombian; two Irish; two Greek; and
one ach Basque, Filipino, Israeli, Japanese, Kurdish, Peruvian, Sri Lankan,
and Turkish. Of course, many hundreds of terrorist groups operate within
national or subnational territories.
FOUNDATIONAL RESEARCH
Although Claire Sterling’s The Terror Network (1981) described links among
Palestinian and Irish Republican Army terrorists and the KBG in the 1970s,
academic researchers began applying social network methods to terrorist
networks after the 9/11 Al-Qaida attacks inside the United States. Foundational efforts were case studies of particular groups or operations, culling
data from newspaper reports and court trial documents, then creating matrix
files for analysis with social network computer programs. Within weeks

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3

after 9/11, Valdis Krebs (2001, 2002) constructed a seminal two-dimensional
diagram (graph) showing the connections among the 19 hijackers and 43
accomplices who provided money, skills, and information. Mohammed
Atta, leader of the field operations, was the central actor and the four pilots
formed a tight clique. Every hijacker was two or fewer steps from two
others, Nawaf Alhazmi and Khalid Almihdhar. The Central Intelligence
Agency knew these two men had participated in a January 2000 planning
meeting with top Al-Qaida leaders in Malaysia. But, the CIA failed to inform
the Federal Bureau of Investigation when the men returned to America
(National Commission, 2004). The Intelligence Community’s failure to track
these suspects was a missed opportunity to detect and possibly apprehend
the 9/11 network before it struck. Krebs (2001) concluded that a “dense
under-layer of prior trusted relationships made the hijacker network both
stealth and resilient,” but “concentrating both unique skills and connectivity
in the same [persons] makes the network easier to disrupt—once it is
discovered.” In another foundational analysis, Marc Sageman (2004) used
biographies of 366 participants in the “global Salafi network” to identify ties
based on kinship, friendship, religious, and work relations. He found four
large clusters built around highly connected “hubs”: Al-Qaida Central Staff,
Maghreb Arabs (North Africa), Core Arabs (Saudi Arabia, Egypt, Yemen,
Kuwait), and Southeast Asians (Indonesia and Malaysia). However, his
only network diagram was schematic, rather than based on actual relations
among specific individuals.
Other network researchers constructed matrices and diagrams of known
connections within terrorist cells. Steven Koschade (2006) found a high density of ties (0.43) among 17 members of a large Jemaah Islamiyah cell that
bombed a Bali, Indonesia, nightclub in 2002. The structure was very centralized, revealing a “mix between efficiency and covertness” (p. 570). Field
commander Samudra and logistics commander Idris had exceptionally high
scores on three centrality measures, which placed both men at the center of
the graph. Samudra had “the highest ability to access others, and the greatest
control over the flow of information in the network . . . . Exclusively due to his
connection to Team Lima [support group], and the suicide bomber Arnasan”
(p. 571). Given the high connectivity inside the cell, detection and capture
of any member by law enforcement might have exposed the entire group
and thwarted the bombing. By comparing four terrorist cells operating in or
against Australia, Koschade (2007) concluded that cells focused on efficiency
rather than covertness were more successful in carrying out attacks. Betweenness centrality (control over information flow) was crucial for identifying cell
leaders. A broader analysis of Jemaah Islamiyah mapped a half-dozen leadership, kinship, and attack networks from 1993 to 2005 (Magouirk, Atran, &
Sageman, 2008).

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

A network of 45 jihadis conducted the March 11, 2004, Madrid train bombings (Jordán, Mañas, & Horsburgh, 2008). Although that operation was
inspired by Osama bin Laden’s explicit threat to target Spain because of its
military involvement in the Iraq War, the participants had few connections
to Al-Qaida. Instead, it was a largely grassroots action organized by three
immigrants of Moroccan and Tunisian origins. Its decentralized cell structure had advantages of “autonomy of operational and tactical command
and control, the capacity to adapt to environments, logistical autonomy, and
protection by way of judicial guarantees” (p. 35). However, lack of professionalism and reliance on open social networks for recruitment, financing,
and arms and explosives made the Madrid operation potentially vulnerable
to detection and disruption. Ami Pedahzur and Arie Perliger (2006) diagrammed four Palestinian networks behind 42 suicide bombings during the
al-Aqsa Intifada and Jewish terrorist networks inside Israel that targeted
Palestinians and assassinated Prime Minister Yitzak Rabin (Pedahzur &
Perliger, 2009). Other researchers applied network analytic methods to the
Turkish Ergenekon terrorist organization (Demiroz & Kapucu, 2012), the
Bakri-Hamza network at London’s Finsbury Park mosque (Horne & Horgan,
2012), a global network of 381 persons affiliated with Islamist organizations
(Medina, 2014), and a Melbourne jihadi cell disrupted before it could launch
an attack (Harris-Hogan, 2013). Common features of these analyses were
formal network measures that identify central, inner circle, and peripheral
actors and plot their positions and connections in diagrams.
A related strand of terrorist research asserted an emerging nexus between
organized criminals and terrorist groups. Terrorists not only engage in
criminal activities to fund operations, but many transact with organized
criminals to buy and sell goods and services, such as weapons and forged
documents. Tamara Makarenko (2005) saw the origins of the nexus in 1980s
Latin American narco-terrorism, exemplified in drug trafficking by the Revolutionary Armed Forces of Colombia (FARC). The terrorism-crime nexus
went global in the 1990s with the ascendancy of transnational organized
crime. Some analysts saw increasing integration (Curtis & Karacan, 2002),
but others doubted whether genuine convergence is feasible (Dishman,
2001; Dandurand & Chin, 2004). This unresolved debate offers opportunities
for future research.
Theorists speculated that counterterror pressures by law enforcement and
military forces compel terrorist groups to change their structures and actions.
In seeking an optimal balance between resilient security and communication
inefficiencies, hierarchical organizations become more decentralized. Brian
Jackson (2006) proposed an evolutionary process from tightly coupled
organization, to coupled network, and thence to loosely coupled movement. Loose-coupling hinders the ability of counterterror organizations

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

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to detect tactical cells and “what actions should be taken to identify and
exploit any vulnerabilities found there” (p. 242). Al-Qaida exemplified
this trajectory from a corporate-like command structure before 9/11 to a
subsequent “leaderless jihad” (Sageman, 2008). But, as decentralized cells
acquire more independence to manage their logistics and select their own
targets, top leaders lose control and efficiency. The shift to weakly connected
network structures risks imperiling the organization’s core mission, when
incompetent rogue cells launch unsuccessful and counterproductive attacks.
CUTTING-EDGE RESEARCH
Mathematicians, game theorists, and computer scientists are dramatically
expanding research beyond foundational case studies of terrorist networks.
Much of their work centers on devising strategies for counterterror organizations to destabilize clandestine organizations. They develop elegant and
precise mathematical models and computer algorithms, then systematically
change parameters to assess capabilities of detecting and disrupting terrorist
activities under varying conditions. Some models are not based on empirical data, but are theory-driven efforts that simulate network dynamics with
artificial datasets. Other models harvest vast quantities of information from
open sources, such as news reports and Websites, then mine these texts for
data patterns that identify key network actors, relations, and properties. This
section discusses a few exemplary cutting-edge models of both kinds.
An early effort applied the mathematical theory of ordered sets to quantify
the extent to which a terrorist group ceases to function when some members
are captured or killed (Farley, 2003). Assuming a hierarchical cell structure of
leaders and followers, the model enables counterterror agencies to estimate
the probability of disconnecting a network by removing a specified number of members. The method involves searching for the network’s cutset, the
network actors whose removal breaks all vertical chains of command linking leaders to foot soldiers. Of course, the mathematical model is moot for
real terrorist groups that are not structured as hierarchical communication
networks. (For related work, see Farley, 2007, 2009 and McGough, 2009.)
Another approach applied graph theoretic metrics to recognize and understand network structural properties. Lindelauf, Borm, and Hamers (2009a)
compared models of covert communication networks to find structures with
optimal trade-offs between two group objectives: secrecy to avoid detection
and operational efficiency of information flow to coordinate and control
cell members. Which model is optimal depends on assumptions about the
likely exposure of all cell members if one person is randomly detected.
For example, a star graph (all cell members communicate only with the
leader) is the optimal structure for balancing the conflicting objectives if the

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

detection of one member also exposes all his links to the other cell members.
In contrast, if the probability of exposure varies by member centrality in the
network, the optimal structures are reinforced rings and reinforced wheel
graphs. Other scenarios making different exposure assumptions and imbalanced secrecy-efficiency trade-offs identify different optimal structures.
(For related models, see Lindelauf, Borm, & Hamers, 2009b, 2010.) Notably
absent from these graph theoretic models are counterterror organizations
that actively try to detect and disrupt the terror networks.
Computational methods allow computer simulations of terrorist networks
and the impacts of alternative counterterror strategies on their resiliency
and capacity to conduct future attacks. Agent-based modeling methods
involve “(i) the simulation of automated agent behaviors and interactions
in the context of their environments; (ii) the analysis of macro-level patterns resulting from micro-level agent interactions” (Keller, Desouza, &
Lin, 2010, p. 1020). By running thousands of simulations under varying
parameter assumptions, researchers can provide some understanding and
insight into potentially effective counterterror strategies against terrorist
networks adapting to their opponents’ actions. An example is the Stochastic
Opponent Modelling Agent (SOMA) package of computational and network tools that used textual data automatically extracted from document
sources to generate rules explaining a terrorist group’s behavior (Sliva,
Subrahmanian, Martinez, & Simari, 2008). Applied to 25 years of monthly
data on the Pakistan-based Lashkar-e-Taiba (LeT), SOMA learned 10 rules
that predicted when LeT was most likely to attack targets in the disputed
Jammu and Kashmir provinces of India and Pakistan (Mannes, Shakarian, &
Subrahmanian, 2011). These rules could “provide accurate probabilistic forecasts for both real and hypothetical situations,” helping policymakers and
counterterror organizations make strategic decisions (p. 6). Other exemplary
data-mining, event-forecasting, link-prediction models, and experimental
methods include Mahesh, Mahesh, and Vinayababu (2010); Arce, Croson,
and Eckel, (2011); Chaurasia, Dhakar, Tiwari, and Gupta (2012); Desmarais
and Cranmer (2013); and Petroff, Bond, and Bond (2013).
Kathleen Carley’s (2003) Dynamic Network Analysis (DNA) package integrated traditional social network analysis of actor-to-actor links with computational multiagent modeling to connect actors, locations, events, tasks,
knowledge, resources, and other elements. It treats terrorist groups as “complex dynamic networked systems that evolve over time” (Carley, 2006, p. 1).
DNA is a “toolchain” of computer programs for collecting extracting data
from texts, mapping networks of words in texts, and forecasting changes.
“Map analysis systematically extracts and analyzes the links between words
in a text in order to model the author’s ‘mental map’ as networks of words”
(Diesner & Carley, 2004, p. 2). Network analytic methods identify actors’

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

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spheres of influence, emergent leaders, and paths among critical actors. Theories of social interaction, such as homophily, can be applied to estimate the
probability of a tie between two actors where no connection is observed.
Analysts can run can virtual DNA experiments, simulating the removal of
actors and observing the consequences. Quantitative measures assess potential immediate and near-term threats from alternative actions by counterterror organizations. Carley (2006) illustrated DNA with an automatic collection of thousands of open-source texts about Iraq and Al-Qaida. The emerging network had a “decidedly cellular structure with 5–12 persons per cell”
(p. 5). Over a decade, it decreased in density and communication levels, but
increased in congruence, suggesting “a movement to a more distributed and
efficient organizational form, possibly with larger cells” (p. 4). One counterterror implication was that removing highly central actors in communication
network would be less effective than taking out key emergent leaders. DNA
software was provided to the Counter-Terror Social Network Analysis and
Intent Recognition (CT-SNAIR) project, which sought to develop automated
tools to “connect the dots” in raw multimedia data by modeling and simulating terrorist networks and attacks (Weinstein, Campbell, Delaney, & O’Leary,
2009, p. 1). A serious limitation was the difficulty in obtaining “truth-marked
data” (p. 13) to test the SNAIR algorithms.
KEY ISSUES FOR FUTURE RESEARCH
Constantly mutating transnational terror networks will shape emerging
trends in the social network analysis of terrorism and counterterrorism.
Although predicting the actions of terrorist groups is notoriously imprecise,
some broad tendencies are discernible. Under pressures by counterterror
organizations, global jihadism evolved during the past two decades from
centralized hierarchies to networked groups, then to fragmented or isolated
cells. Disconnected units are more difficult to detect and disrupt, especially
lone-wolf attacks (Borum, Fein, & Vossekuil, 2012), such as the November 9,
2009, Fort Hood shooting and the April 15, 2013, Boston Marathon bombing.
Unstable and failed states increasingly offer sanctuaries for terrorists to
assemble, train, plan, and launch operations, such as the September 21, 2013,
attack by Al-Shabaab gunmen from Somalia on Westgate Mall in Nairobi,
Kenya. Insurgencies and guerilla wars, flaring across Libya, Mali, Yemen,
Sudan, the Sinai, Syria, and other parts of the Middle East and North Africa,
offer training grounds for terrorist organizations and their foot soldiers to
acquire arms, weapon skills, and combat experience. With the impending
2014 withdrawal of U.S. and NATO forces from Afghanistan, the Taliban,
Al-Qaida, and “a dozen like-minded groups … are slowly and steadily
returning to Afghanistan, re-creating the pre-9/11 sanctuary” (Gunaratna,

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

2013, p. 2). Although the U.S. and its allies decimated the original Al-Qaida
top leadership, in the past five years, proliferating affiliate groups “have
unquestionably expanded their operational reach and capability … We can
be certain, however, that they will have much more extensive resources and
capabilities than any group that has yet tried to attack us, if and when they
do” (Kagan, 2013, pp. 5–6). Transnational terror will likely plague the planet
into the foreseeable future.
Trends in transnational counterterrorism will likely continue the brutal,
lethal, and sometimes illegal government strategies and tactics that emerged
after 9/11 (Kurtulus, 2011). The Bush Administration’s prosecution of “the
global war against terror” violated the Geneva Conventions on torture and
human rights, greatly expanded the national security state, and harmed
domestic civil liberties (Chadwick, 2003; Liese, 2009). The Obama Administration curbed some egregious abuses of power, for example, closing
the CIA’s secret “black site” prisons and banning coercive interrogation
methods. But, it extended other Bush counterterror policies and practices,
such as asserting the state secrets privilege and federal immunity from
lawsuits on behalf of tortured victims of U.S. renditions (Cole, 2010).
Targeted killings of suspected terrorists by drone strikes in Afghanistan,
Pakistan, and Yemen increased fivefold, with high numbers of civilian
casualties, euphemistically called collateral damage. Obama greatly expanded
the National Security Agency’s phone and Internet surveillance programs
(Greenwald, 2013). By creating a chain of contacts from massive metadata
mining, NSA seeks “to identify hubs or common contacts between targets
of interest who were previously thought to be unconnected, and potentially
to discover individuals willing to become US Government assets” (Lizza,
2013, p. 57). After former NSA contractor Edward Snowden revealed the
vast scope of data-dredging, one federal judge ruled the program an unconstitutional violation of privacy: “No court has ever recognized a special need
sufficient to justify continuous, daily searches of virtually every American
citizen without any particularized suspicion” (Nakashima & Marimow,
2013). But, another judge ruled it legal. Whatever the ultimate outcome of
judicial appeals, presidential reviews, and congressional restrictions, cyber
snooping will undoubtedly remain a counterterror priority for detecting
dubious data.
Scholars in the interdisciplinary field of terrorism studies too often trail
behind event-driven trends in transnational terrorism. To get ahead of the
curve, researchers must look beyond investigating contemporary incidents
to understanding broader contexts and longer-range perspectives. Some key
issues and opportunities for future network research include:
Conduct rigorous comparative analyses of four historical waves of modern
terrorism for clues about the present and future waves. An anarchist wave,

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

9

beginning in late-19th century, was followed by anti-colonialist, New Left,
and the contemporary jihadist wave (Rapoport, 2001). Comparing each
wave’s long-term network dynamics will yield important contrasts and
insights into their similar and unique trajectories. One result will be better
understanding of the origins of terror campaigns, responses of counterterror
organizations, innovation and evolution of strategies and tactics, and
processes of desistance that bring terrorism to an end.
Build more comprehensive, cohesive, and integrated theoretical models capable of explaining the formation, structure, and consequences of
terrorist networks. Analytic models of network dynamics must explicate
the interpersonal processes by which people are recruited to clandestine organizations, trained in nefarious skills, allocated to organizational
positions, and assigned roles in terror operations. Elements for building
social network theories of terrorism will be drawn from diverse social
science disciplines, encompassing psychological, sociology, geographic,
political, economic, and related paradigms. Connecting these elements
necessitates close collaborations among substantive experts. Generating
testable hypotheses will benefit from the participation of researchers from
the computational sciences. Barriers to effective interdisciplinary research
must be overcome, particularly the lack of understanding of alternative
professional perspectives and incompatible taken-for-grant assumptions.
Develop new methods of measuring network relations among terrorists. In
addition to improving the accuracy of automated text analysis techniques,
how will more reliable information be extracted from photographic, video,
and audio recordings? Will security software, such as biometric authentication and face-recognition software, be adapted to generate new network
data? How will these diverse modes of data collection be effectively integrated using network analytic methods?
Perform more laboratory experiments as an alternative to collecting
inaccessible and dangerous field observation data. Researchers will
construct theoretically based models of interdependent terrorist and counterterror networks comprising both computer programs and human subjects.
Controlled manipulation of parameters, such as information and costs, will
test hypotheses predicting actor reactions and network structural changes.
Investigators will study the impacts of varying scenarios on subjects’ actions
and collective outcomes such as detection, deterrence, disruption, network
resilience, security decision, resource allocation, target selection, and attack
success. For greater complexity and realism, experimental findings will be
adapted to massively multiuser online role-playing games pitting virtual
terrorists against counterterror agents.
Create large, high-quality relational datasets to test social network theories of terrorism. Researchers will shift from case studies of particular

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

events to encompassing systems of people, organizations, institutions, and
events. Counterterror actions will be integrated with terrorist behaviors
to create more realistic coevolving network dynamics. Given the paucity
of primary data collected from terrorists, many researchers will continue
to depend on collecting secondary data from public documents. Other
analysts will emphasize the importance of the Internet and cyberspace
communication networks linking thousands of extremist Websites for propaganda, radicalization, recruitment, and financial transactions. Vastly more
sophisticated massive data-mining algorithms will improve content-based
pattern detection. But, quality assurance will necessitate such automated
routines be supplemented by painstaking hands-on correction of gaps and
errors.
Regardless of specific future directions, social network researchers must
surely rise to the challenge of how to use network analytic theory and
methods for better understanding, detecting, and thwarting of miscreants
engaged in terrorist activities.
REFERENCES
Arce, D. G., Croson, R. T. A., & Eckel, C. C. (2011). Terrorism experiments. Journal of
Peace Research, 48, 373–382. doi:10.1177/0022343310391502
Borum, R., Fein, R., & Vossekuil, B. (2012). A dimensional approach to analyzing
lone offender terrorism. Aggression and Violent Behavior, 17, 389–396. doi:10.1016/
j.avb.2012.04.003
Carley, K. M. (2003). Dynamic network analysis. In R. Breiger, K. Carley & P. Pattison
(Eds.), Dynamic social network modeling and analysis: Workshop summary and papers
(pp. 133–145). Washington, DC: Committee on Human Factors, National Research
Council.
Carley, K. M. (2006). A dynamic network approach to the assessment of terrorist groups and the impact of alternative courses of action. In Visualising Network Information. Meeting Proceedings RTO-MP-IST-063, Keynote 1 pp.
KN1-1–KN1-10. Neuilly-sur-Seine, France: RTO. Retrieved from http://www.
vistg.net/documents/IST063_PreProceedings.pdf.
Chadwick, E. (2003). It’s war, Jim, but not as we know it: A ‘reality-check’ for international laws of war? Crime, Law and Social Change, 39, 233–262. doi:10.1023/
A:1022907030010
Chaurasia, N., Dhakar, M., Tiwari, A., & Gupta, R. K. (2012). A survey on terrorist network mining: Current trends and opportunities. International Journal of
Computer Science & Engineering Survey, 3(4) Retrieved from http://www.airccse.
org/journal/ijcses/papers/3412ijcses05.pdf. doi:10.5121/ipcses.2012.3405
Cole, D. (2010). Breaking away. New Republic December 30. Retrieved from
http://www.newrepublic.com/article/magazine/politics/79752/breakingaway-obama-bush-aclu-guantanamo-war-on-terror.

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

11

Curtis, G., & Karacan, T. (2002). The Nexus among terrorists, narcotics traffickers, weapons proliferators, and organized crime networks in Western Europe. Washington, DC: Federal Research Division, Library of Congress. Retrieved from
http://www.loc.gov/rr/frd/pdf-files/WestEurope_NEXUS.pdf.
Dandurand, Y., & Chin, V. (2004). Links between terrorism and other forms of crime.
Vancouver, BC: International Centre for Criminal Law Reform and Criminal
Justice Policy. Retrieved from http://icclr.law.ubc.ca/sites/icclr.law.ubc.ca/files/
publications/pdfs/LinksBetweenTerrorismLatest_updated.pdf.
Demiroz, F., & Kapucu, N. (2012). Anatomy of a dark network: the case of the
Turkish Ergenekon terrorist organization. Trends in Organized Crime, 15, 271–295.
doi:10.1007/s12117-012-9151-7
Desmarais, B. A., & Cranmer, S. J. (2013). Forecasting the locational dynamics
of transnational terrorism: A network analytic approach. Security Informatics, 2,
1–13. Retrieved from http://www.security-informatics.com/content/pdf/21908532-2-8.pdf. doi:10.1186/2190-8532-2-8
Diesner, J., & Carley, K. M. (2004). Using network text analysis to detect the organizational structure of covert networks (Retrieved from http://citeseerx.ist.psu.edu/
viewdoc/download?doi=10.1.1.206.5240&rep=rep1&type=pdf).
Dishman, C. (2001). Terrorism, crime, and transformation. Studies in Conflict & Terrorism, 24, 43–58. doi:10.1080/10576100118878
Farley, J. D. (2003). Breaking Al Qaeda cells: A mathematical analysis of counterterrorism operations (A guide for risk assessment and decision making). Studies in
Conflict & Terrorism, 26, 399–411. doi:10.1080/10576100390242857
Farley, J. D. (2007). Toward a mathematical theory of counterterrorism: Building the perfect terrorist cell. Proteus Monograph Series Volume 1 Issue 2. Retrieved from
http://www.rit.edu/∼w-cmmc/literature/Proteus.pdf.
Farley, J. D. (2009). Two theoretical research questions concerning the structure of
the perfect terrorist cell. In N. Memon, J. D. Farley, D. L. Hicks & T. Rosenorn
(Eds.), Mathematical Methods in Counterterrorism (pp. 91–103). New York, NY:
Springer-Verlag/Wien.
Greenwald, G. (2013). NSA collecting phone records of millions of Verizon customers daily. The Guardian June 5. Retrieved from www.guardian.co.uk/world/
2013/jun/06/nsa-phone-records-verizon-court-order.
Gunaratna, R. (2013). Terrorists to bounce back in 2013. Counter Terrorist Trends and
Analysis, 5, 2–4.
Harris-Hogan, S. (2013). Australian neo-jihadist terrorism: Mapping the network and
cell analysis using wiretap evidence. Studies in Conflict & Terrorism, 35, 298–314.
doi:10.1080/1057610X.2012.656344
Horne, C., & Horgan, J. (2012). Methodological triangulation in the analysis of terrorist networks. Studies in Conflict & Terrorism, 35, 182–192. doi:10.1080/1057610X.
2012.639064
Jackson, B. A. (2006). Groups, networks, or movements: A command-and-controldriven approach to classifying terrorist organizations and its application to Al
Qaeda. Studies in Conflict & Terrorism, 29, 241–262. doi:10.1080/10576100600564042

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Jordán, J., Mañas, F. M., & Horsburgh, N. (2008). Strengths and weaknesses of grassroot jihadist networks: The Madrid bombings. Studies in Conflict & Terrorism, 31,
17–39. doi:10.1080/10576100701767148
Kagan, F. W. (2013). The continued expansion of al Qaeda affiliates and their
capabilities. Washington: Testimony before the House Committee on Foreign
Affairs Subcommittee on Terrorism, Nonproliferation, and Trade. Retrieved from
http://www.aei.org/speech/foreign-and-defense-policy/terrorism/al-qaeda/
the-continued-expansion-of-al-qaeda-affiliates-and-their-capabilities.
Keller, J., Desouza, K. C., & Lin, Y. (2010). Dismantling terrorist networks: Evaluating strategic options using agent-based modeling. Technological Forecasting & Social
Change, 77, 1014–1036. doi:10.1016/j.techfore.2010.02.007
Koschade, S. (2006). A social network analysis of Jemaah Islamiyah: The applications
to counterterrorism and intelligence. Studies in Conflict & Terrorism, 29, 559–575.
doi:10.1080/10576100600798418
Koschade, S. (2007). The internal dynamics of terrorist cells: A social network analysis of terrorist cells in an Australian context. Unpublished doctoral dissertation, Queensland University of Technology, Brisbane, Australia. Retrieved from
http://eprints.qut.edu.au/16591.
Krebs, V. (2001). Mapping networks of terrorist cells. Connections, 24, 43–52. Retrieved
from http://vlado.fmf.uni-lj.si/pub/networks/doc/Seminar/Krebs.pdf.
Krebs, V. (2002). Uncloaking terrorist networks. First Monday, 7, 1–14. Retrieved from
http://firstmonday.org/ojs/index.php/fm/article/view/941.
Kurtulus, E. N. (2011). The new counterterrorism: Contemporary counterterrorism
trends in the United States and Israel. Studies in Conflict & Terrorism, 35, 37–58.
doi:10.1080/1057610X.2012.631456
Liese, A. (2009). Exceptional necessity: How liberal democracies contest the prohibition of torture and ill-treatment when countering terrorism. Journal of International
Law and International Relations, 5, 17–47.
Lindelauf, R., Borm, P., & Hamers, H. (2009a). The influence of secrecy on the communication structure of covert networks. Social Networks, 31, 126–137. doi:10.1016/
j.socnet.2008.12.003
Lindelauf, R., Borm, P., & Hamers, H. (2009b). On heterogeneous covert networks.
In N. Memon, J. D. Farley, D. L. Hicks & T. Rosenorn (Eds.), Mathematical methods
in counterterrorism (pp. 215–228). New York, NY: Springer-Verlag/Wien.
Lindelauf, R., Borm, P., & Hamers, H. (2010). One-mode projection analysis and
design of covert affiliation networks. Tilburg, Netherlands: Tilburg University Center for Economic Research, Discussion Paper 2010–53. Retrieved from
https://pure.uvt.nl/portal/files/1225410/2010-53.pdf.
Lizza, R. (2013). State of deception: Why won’t the president rein in the intelligence
community? New Yorker, December, 16, 48–61.
Magouirk, J., Atran, S., & Sageman, M. (2008). Connecting terrorist networks. Studies
in Conflict & Terrorism, 31, 1–16. doi:10.1080/10576100701759988
Mahesh, S., Mahesh, T. R., & Vinayababu, M. (2010). Using data mining techniques
for detecting terror-related activities on the Web. Journal of Theoretical & Applied
Information Technology, 16, 99–104.

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism

13

Makarenko, T. (2005). Terrorism and transnational organized crime: Tracing the
crime-terror nexus in South East Asia. In P. Smith (Ed.), Terrorism and violence in
South East Asia: Transnational challenges to states and regional stability (pp. 169–187).
New York, NY: M. E. Sharpe.
Mannes, A., Shakarian, J., & Subrahmanian, V. S. (2011). A computationally-enabled
analysis of Lashkar-e-Taiba attacks in Jammu and Kashmir. In Intelligence and
Security Informatics Conference (EISIC), 2011 European (pp. 224–229. Retrieved
from http://shakarian.net/janaPapers/let_eisic_camera.pdf. doi:10.1109/EISIC.
2011.61).
McGough, L. R. (2009). Mathematically modeling terrorist cells: Examining the
strength of structures of small sizes. In N. Memon, J. D. Farley, D. L. Hicks & T.
Rosenorn (Eds.), Mathematical methods in counterterrorism (pp. 55–67). New York,
NY: Springer-Verlag/Wien.
Medina, R. M. (2014). Social network analysis: A case study of the Islamist terrorist
network. Security Journal, 27, 97–121. Retrieved from. doi:10.1057/sj.2012.21
Nakashima, E., & Marimow, A. E. (2013). Judge: NSA’s collecting of phone records is
probably unconstitutional. Washington Post December 16. Retrieved from http://
www.washingtonpost.com/national/judge-nsas-collecting-of-phone-records-islikely-unconstitutional/2013/12/16/6e098eda-6688-11e3-a0b9-249bbb34602c_
story.html.
National Commission on Terrorist Attacks Upon the United States (2004). The
9/11 commission report. New York, NY: W.W. Norton. Retrieved from http://
www.gpo.gov/fdsys/pkg/GPO-911REPORT/pdf/GPO-911REPORT.pdf.
Pedahzur, A., & Perliger, A. (2006). The changing nature of suicide attacks: A social
network perspective. Social Forces, 84, 1987–2008. doi:10.1353/sof.2006.0104
Pedahzur, A., & Perliger, A. (2009). Jewish terrorism in Israel. New York, NY: Columbia
University Press.
Petroff, V. B., Bond, J. H., & Bond, D. H. (2013). Using hidden Markov models to predict terror before it hits (again). In V. S. Subrahmanian (Ed.), Handbook of computational approaches to counterterrorism (pp. 163–180). New York, NY:
Springer-Verlag/Wien.
Rapoport, D. C. (2001). The fourth wave: September 11 in the history of world terrorism. Current History, 100, 419–424.
Sageman, M. (2004). Understanding terror networks. Philadelphia: University of Pennsylvania Press.
Sageman, M. (2008). Leaderless jihad: Terror networks in the twenty-first century.
Philadelphia: University of Pennsylvania Press.
Sliva, A., Subrahmanian, V. S., Martinez, V., & Simari, G. I. (2008). The SOMA Terror
Organization Portal (STOP): Social network and analytic tools for the real-time
analysis of terror groups. First International Workshop on Social Computing,
Behavioral Modeling, and Prediction. Retrieved from http://content.schweitzeronline.de/static/content/catalog/newbooks/978/038/777/9780387776712/
9780387776712_Excerpt_001.pdf.
Sterling, C. (1981). The terror network: The secret war of international terrorism. New
York, NY: Holt, Rinehart and Winston.

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

U.S. Department of State (2013). Country reports on terrorism 2012. Retrieved from
http://www.state.gov/j/ct/rls/crt/2012.
Weinstein, C., Campbell, W., Delaney, B., & O’Leary, G. (2009). Modeling and detection techniques for counter-terror social network analysis and intent recognition.
Institute of Electrical and Electronics Engineers. Retrieved from dspace.mit.edu/
openaccess-disseminate/1721.1/71803.

FURTHER READING
Everton, S. F. (2012). Disrupting dark networks. New York, NY: Cambridge University
Press.
McCulloh, I., Armstrong, H., & Johnson, A. (2013). Social network analysis with applications. New York, NY: John Wiley & Sons, Inc.
Memon, N., Farley, J. D., Hicks, D. L., & Rosenorn, T. (Eds.) (2009). Mathematical methods in counterterrorism. New York, NY: Springer-Verlag/Wien.
Mullins, S. (Ed.) (2013). Special issue: Applying social network analysis to terrorism.
Behavioral Sciences of Terrorism & Political Aggression, 5(2), 67–175.
Ranstorp, M. (Ed.) (2007). Mapping terrorism research: State of the art, gaps and future
direction. New York, NY: Routledge.
Subrahmanian, V. S. (Ed.) (2013). Handbook of computational approaches to counterterrorism. New York, NY: Springer-Verlag/Wien.

DAVID KNOKE SHORT BIOGRAPHY
David Knoke is professor of sociology at the University of Minnesota, where
he teaches courses in statistics, networks, and organizations. He received his
PhD in 1972 from the University of Michigan and was professor of sociology
at Indiana University from 1972 to 1985. He was a Fulbright research scholar
at Kiel University (1989) and a fellow at the Center for Advanced Study in the
Behavioral Sciences (1992). In 2008, he received the University of Minnesota
College of Liberal Arts’ Arthur “Red” Motley Exemplary Teaching Award.
With various colleagues, he received several National Science Foundation
research grant and published the results in research monographs on political, organizational, and social network behavior. Some of these books are The
Organizational State, Organizing for Collective Action, Political Networks, Organizations in America, Comparing Policy Networks, Changing Organizations, Social
Network Analysis, and Economic Networks. His current research investigates
diverse social networks, including intra- and interorganizational, health care,
economic, financial, terrorist, and counterterror networks.
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