15 October 2012
Dr. Gordon M. Hahn
IIPER Founder, Editor, and Author (unless otherwise noted), and
Senior Associate, Russia and Eurasia Program, Center for Strategic and International Studies
See Gordon M. Hahn’s most recent article “The Caucasus Emirate Jihadists: The Security and Strategic Implications,” in Stephen J. Blank, ed., Russia’s Homegrown Insurgency: Jihad in the North Caucasus (Carlisle Barracks, PA: U.S. Army War College Strategic Studies Institute, October 2012), pp. 1-98, http://www.strategicstudiesinstitute.army.mil/pdffiles/PUB1116.pdf.
* IIPER is written and edited by Dr. Gordon M. Hahn unless otherwise noted. IIPER accepts outside submissions from other analysts when it sees fit, with the caveat that Dr. Hahn does not necessarily agree with the all the assumptions or conclusions put forward by guest authors. Research assistance is provided by Anna Nevo, Casey Mahoney, Daniel Painter, Elizabeth Wolcott, Jerry Davydov, Kevin Butts, Michelle Enriquez, Olga Volcsko, and Stephanie Barko.
“Two-mode Social Network Analysis of the Caucasus Emirate:
Subgroups, and Violent Incidents in the Northern Caucasus”
by Seth Gray
[Edited and Supervised by Gordon M. Hahn]
In order to delve into the intricacies of the Northern Caucasian insurgent and terrorist organization, the Caucasus Emirate (CE), a look at several driving dynamics is important. Through a combination of geospatial data and social network analysis, we can analyze the intense localization of violence and question the CE’s network structure. This is the first attempt at looking at the CE through a combination of analyses that better illuminates the relationships between the CE network’s groups and their alignment with the CE. This first attempt also narrows down on Dagestan as it is well known to be the epicenter of violence in the Northern Caucasus. As other locations have proportionally declined in violence, Dagestan has proportionally increased. There are several factors, which are explored later on that are driving this increase in violence in Dagestan.
A major concern is that historically, violence has been difficult to mitigate in more heterogeneous areas of the Northern Caucasus. The CE has used ethnic, clan, and religious tensions to create a greater disruption, thus fomenting insurgent activities. Dagestan is the least homogenous of the region’s republics, offering a greater variety of ethnicity and religion. As time passes, the ability to mitigate violence within the area will lessen and a greater chance of the spread of violence into other regions as well as other countries will increase. More complex mitigating tactics and strategies will be required in order to reduce violence within the area as well as reduce the operational capacity of the Caucasus Emirate.
Geography and Ethnicity
Geography and ethnicity play large roles in how the Caucasus Emirate and its sub-groups operate within each of its spheres of operation. These spheres are represented by the CE’s four main vilaiyats: Vilaiyat Nokchikcho (Chechnya), Vilaiyat Gyalgyaiche (Ingushetia and Ossetia), Vilaiyat Dagestan, and the United Vilaiyat of Kabardia, Balkaria, and Adygea. Each of these vilaiyats are comprised of a number of sectors in turn comprised of combat jamaats and special operational groups comprised of a single ethnic group or multiple ethnicities dependent upon the geographic location.
Dagestan consists of sixty indigenous ethnicities and has representatives of 102 different ethnic groups. The largest group, the Avars, along with the Dargins, Lezgins, Laks, Tabasarans, and Chechens belong to Ibero-Caucasian family of languages. The Nakh-speaking people represent the largest collection of ethnicities. There is also the Altai family of languages represented by Kumyks, Nogais and Azeris and the Tat-based language (related to Farsi) represented by Russians, Tats, and mountain Jews. To add to the complexity is Dagestan’s village jamaat structure, which differs from the Chechen teip system. Jamaats are characterized by a collection of geographically contiguous villages tied together by tukhumy or large, tribe-like clan. Historically, these jamaats have governed territories and protected the lands in which these villages are settled. Political structures in Dagestan are for the most part powerless. Strong-man politics, individual actors operating within the greater political context are the main impetus with which negotiations occur and deals are made. They are also the focal points for frustrations as they are commonly targeted instead of the institutions themselves.
This gives rise to the potentiality of a high localization of violence as a jamaat or a collection of jamaats are endowed to maintain their area of operations which is commonly a village, an administrative district or in some cases a city e.g. Makhachkala. Kisriev describes these groups as “ethnoparties.” The old definition of jamaat (localized, ethnically homogenous chain of villages) is supplanted by a newer, slightly modified one. The jamaats that are mostly responsible for the violence today comprise members of single and multiple ethnic groups “concerned primarily with local interests.” These highly cellular and localized units of society could be equated with that of a porous honeycomb. Although each jamaat is unique, it can be related to other units through language, extended blood and kinship, and religion. Unfortunately, there is no data on the ethnic makeup of specific jamaats that comprise the CE but it cannot be ruled out that these groups exploit ethnic and sociocultural divisions within the populations they operate in. For example, throughout 1989 there were a series of interethnic and interpolitical disputes which demonstrated that a regional Islamic Congress would be impossible to create. As a result of these quarrels and the dissolution of the Soviet Union, a vacuum in power had emerged resulting in a highlighted fragmentation of society and the movement of traditional jamaats and ethnoparties to fill the void. The members that comprise “ethnoparties” may form alliances with other parties and other jamaats to address local issues.
Another conflict in May of 1997 may exemplify these extremely complicated social structures that form and dissolve so rapidly. In the Dagestan the ‘Kadar zone’ consisisting of Kadar, Chabanmakhi, and Karamakhi villages, a dispute occurred between Sufi and Wahhabi Muslims over simple protocol of prayer during a funeral, which resulted in the deaths of multiple civilians and a standoff that lasted until the Interior Military Special Task Force (OMON) blockaded access to the village, thus isolating the tensions and stopping outsiders from joining the fray. Flash groups are fairly endemic to the Caucasus as they address local issues that require quick resolve. In regards to the conflict in Chabanmakhi, the Wahhabis decided to control the roads passing through their area of operation. A proclamation was issued by Dagestani authorities, declaring force was necessary to quell violence and mobilizing surrounding village jamaats against the ‘Wahhabis’. This decree, a hierarchical approach to an asymmetric and convoluted conflict essentially fell flat as these groups dissolved and then reformed in late December of 1997 to form the “Military Mutual Assistance Treaty” which involved Chechen government forces and the self-styled independent state, the Islamic Jamaat of Dagestan.’
Keeping these factors in mind, let us turn to our examination of the CE network and violence, using various geospatial and social network methodologies. Data used for calculating hotspots and other measures was retrieved from Jane’s Insurgency and Terrorism Database Event Center. This data consists of violent incidences occurring from January 1st 2007 to December 31st 2011. The database does not parse data according to specific attributes such as fatality and casualty rates for specific actors such as security forces, non-state combatants and civilians, or for methods of attack. This data was hand coded given the summary of each event generated by the database. Subsequently, this data was uploaded to a Palantir investigation and combined with relational data garnered from Dr. Gordon Hahn’s Islam, Islamism and Politics in Eurasia Report (IIPER). Palantir is a proprietary software platform used for information analysis and it allows streamlined data integration processes necessary for data aggregation. Palantir allows the combination of structured data (Excel tables) and unstructured data (doc, PDF, jpeg) upon which a user may combine attribution data and if desired, the export of this data for deeper and more refined analyses. After aggregation within Palantir, the data was then exported into a file format readable by ArcGIS, a suite of geographic information system (GIS) used for multiple forms of geospatial analysis.
Jane’s Database attributes all violence North Caucasus political violence to the CE due to data aggregation purposes. But as discussed earlier, the violence can not be completely attributed to one specific group and must be further defined. In order for all the violence to be attributed to CE, the 28 jamaats in this study would have to claim complete allegiance as well as work with each other in concert. A prime example is the dissonance that occurred between the subgroups of Doka Umarov and Hussein Gakaev in the summer of 2010. After Umarov publicized he would be stepping down as acting Emir and then rescinded the change of leadership to members within the Nokhchicho Villaiyat, Gakaev effectively splintered the group from allegiance with CE and commited subsequent acts of violence independently. Although Gakaev reunited with Umarov months later, this act highlights the ability of these groups to act autonomously, focusing on localized issues such as the governence of territory. Localization also indicates that groups specifically have areas of operation and remain anchored to these spots. Some groups have been documented to move to distances outside of their areas as evidenced by al-Khattab’s International Islamic Brigade. However, his brigade was composed mainly of foreign fighters who did not have ties to a local village. They were mobile in nature. The spatial autocorrelation measure (Moran’s I) found the clustering at an index of 0.03 with a z-score of 1.96. This indicates that there is less than a 1% chance that the acts are occuring at specific locations by random chance. There was no indication that one ethnicity was reposonsible for more violence than others, but more acts fell into historically controlled Avar and Chechen territories even when normalized for area. Another trend that was found was the gradual eastward movement of the distribution of violence. From 2007 onward, the distribution of violence has moved eastward towards Dagestan, ending with the majority of violence in Dagestan territory in 2011. These ellipses signify a geographical mean center as well as directionality. Factors that may contribute to the eastward movement of the geographical mean center are the suppression of Chechen jamaats by Ramzan Kadyrov. Under Kadyrov, violence has reached unprecedented lows. Another causal factor is that Dagestani jamaats are outperforming Chechen and Ingushetian jamaats. These ellipses only show gradual trends in dispersion and do not highlight individual jamaat behavior temporally.
The Caucasus Emirate as a Dark Network
Departing from a geospatial perspective, we turn to social network analysis in order to point out with greater detail, the structure of the CE as well as illuminate future possibilities as to the composition and location(s) of the network. The CE is a dark network. It operates in secrecy. Dark networks are typically illicit organizations that hide themselves from authorities and can range from terrorist and criminal organizations but they can be networks that simply want to remain unknown. Sean Everton of the Naval Post Graduate School notes the study of dark networks has increased after 9/11 and social network analysis (SNA) has become a tool of choice for governments wishing to analyze criminal, terrorist and insurgent groups. Dark networks offer a greater challenge to analysts because of a lack of data. Because they operate clandestinely and because much of the information gathered is classified, open-source methods have their limitations. As a result, the limitations of SNA are noted in the small amount of statistically significant analyses performed. However, given the data and in conjunction with other combined qualitative and quantitative analyses, we can shed some light on this network and unravel some of the complexities as to the causes of violence and the actors involved.
First and foremost, let us first discuss what social network analysis is and the analytical tools it will bring to this analysis. SNA has been around since the 1930’s stemming from the work of Jacob Moreno. Moreno created sociograms, diagrams used in the analysis of people and their relationships. From “Sociometrics” to current Social Network Analysis, SNA is an analysis of a network of entities: people, groups, events, intuitions, etc. and how these entities interact with each other within a network structure and over time. Through analysis, key nodes (actors) are identified as well as their influence on other nodes. A recent contribution from the social sciences, SNA has been used in research within a multitude of disciplines, from identifying key trading partners and streamlining organizational structures in large corporations to mapping in order to disrupt criminal and terrorist organizations. For this study, three individual SNA platforms were used: Pajek, UCINET, and ORA (Organizational Risk Assessment). Each program should be noted as having both strengths and weaknesses and were utilized in tandem to overcome weaknesses they may generate in any analysis if used alone.
Pajek, which means “spider” in Slovenian, was written in 1996. Created by Vladimir Batagelj and Andrej Mrvar at the University of Ljublijana, Pajek was initially designed to analyze organic molecules in Chemistry and protein-receptor interaction networks but quickly became of use to the Social Sciences. Pajek enables an analyst to separate components and subgroups from a large network, highlight links between them, and visualize them using subquadratic algorithms. UCINET was developed by Steve Borgatti, Martin Evererett, and Lin Freeman out of the University of Irvine, California. Although it cannot handle datasets within the millions as Pajek can, it offers some measures not present in Pajek and which have been employed in this study. It also utilizes an external program, NetDraw, to visualize datasets. The last SNA platform, ORA, was developed at Carnegie-Melon and hosts similar measures but also allows geospatial measures such as Moran’s I and Gary’s C to run if geospatial attributes are uploaded. After data was exported from the Palantir investigation, there was a serious lack of relational data between individual actors. There was data pertaining to top CE leadership but there wasn’t enough data to correlate with the amount of violent incidences. This indicates either a lack of data as just mentioned, or a greater amount of variance in the causes of violence. Again, we must question how pervasive and how contributive the role of the CE is in the republics and question how we can create a better ontology as to defining violence. So, a 2-mode network analysis was chosen given the amount of data in which two categories are utilized instead of one i.e. agent by agent to agent by event. Instead, a jamaat by event network was used. A two-mode approach analyzes two kinds of entities such as actors and events, which are employed within this thesis. Within an affiliation matrix, rows represent actors and columns represent events. Signifying a relationship between an actor and an event is defined by a numerical value such as 1. So, as a matrix is filled out, a 1 is marked within the row where the actor meets the event, or i meets j. This graph is used when the researcher can suppose that actor(s) participate in the event, which indicates “a potential for some form of social bond between them.” Building on the work of Borgatti and Everett, a two-mode approach and normalization is utilized to account for the adjacency of nodes. For degree centrality (the amount of events and actor is tied to), the following equations are defined as:
In other words, the degree centrality for an actor is the number of events tied to that particular actor and the opposite for an event. In this, the actor’s degree is taken and then divided by the number of events and an event’s degree is taken and divided by the number of actors. This process is done for each event and each actor. Output is commonly generated as indegree and outdegree. In this context, outdegree is used because the relationship is directed, meaning the jamaat is the cause of the event. In order to link an event to a jamaat, areas of operation were constructed by using administrative districts controlled by specific jamaat(s). Because areas of operation are not restricted to the boundary of an administrative district, an additional 10km were given to simulate mobility. Events that fall within the established proximities are then linked to the jamaat. Out of 1554 events, 532 were connected to jamaats by proximity and the remaining isolates were removed from the data set for the time being.
Figure 5 shows the graph for the North Caucasus in its entirety (552 events connected to 28 jamaats). Isolates were removed due to visualization purposes. As one can visually derive from this graph, the network is fairly sparse, meaning that there is a small percentage of ties out of the entirety of possible connections, e.g. every node is connected to every node. This is fairly normal given the data that has been worked with. We can also see that the network has many sub-groups. This is also normal given the overlap of operational areas. From here the network is conflated from a 2-mode network, into a 1-mode. Currently, there is a serious lack of 2-mode analytical tools and the network is converted in order to run 1-mode centrality measures et al. After the network is conflated and then binarized, the appropriate centralities can be run:
Figure 6. Conflated and binarized network with a Fruchterman-Reignold layout of the Caucasus Emirate network (see Figure 5 for the previous 2-mode network).
Degree Centrality Measures
Figure 7. UCINET output for Degree Centrality
|FREEMAN’S DEGREE CENTRALITY MEASURES|
|14||Mujahideen unit Grozny||86||86||0.386||0.386|
|18||Sunzha Ingush Jamaat||80||80||0.359||0.359|
|9||Shali Sector Jammat||44||44||0.197||0.197|
|5||Asam-yurt Village Jamaat||41||41||0.184||0.184|
|17||Sunzha Chechen Jamaat||39||39||0.175||0.175|
|11||Tevzan Village Jamaat||35||35||0.157||0.157|
|10||Shatoi Sector Jamaat||18||18||0.081||0.081|
|12||Khatunni village Jamaat||16||16||0.072||0.072|
|2 Std Dev||3.464||3.464||14.434||14.434|
|7 Euc Norm||34.641||34.641||144.338||144.338|
|10 N of Obs||25||25||25||25|
|Network Centralization (Outdegree)||= 39.063%|
|Network Centralization (Indegegree)||=39.063%|
Given the output of Freeman’s Degree Centrality, three of the top five most active jamaats are located in Dagestan. In an information network where ties were determined by information sharing, Shamikala-Kaspisk jamaat and Jamaat Tavaakul would be considered the most influential. One thing that must be accounted for is that areas of operation vary from jamaat to jamaat and the smaller the area; the likelihood of clustering is greater. Another factor that cannot be visualized is the rate at which jamaats share fighters and if a jamaat may claim responsibility for an attack occurring in another area under different jurisdiction. Data garnered from IIPER reports suggests that this is a regular occurrence. However, this data was so sparse that it could not be factored into this analysis of 552 events to be significantly viable. Given that the network centralization for the entire network is 39.063%, we can conclude that there is a moderate amount of concentration or centralization within the entire network i.e. the power of specific jamaats within the entire network varies moderately but this does not suggest that one jamaat may be emphasized over another in terms of importance, power, or authority.
Figure 8. Bonacich output
The Bonacich output differs slightly with the traditional degree centrality measure. The original degree centrality looked at a network as “he who has the most connections wins” or the most connected nodes as being the most powerful. Bonacich degree centrality factors in the centrality of the nodes that a particular node is connected to and avoids the possible misinterpretation of the network. For example, node A may have the most connections but node B is tied to nodes C and D who boast high degree centralities of their own. Basically, this measure takes into account the connections of your connections. From Figure 8, we can see that the Chechnya’s Urus-Martanovskiy Jamaat has the highest measure given that it is connected to other Chechen jamaats that have been very active. Although the Dagestan Vilaiyat’s
|9||Shali Sector Jammat||10||1.443|
|5||Asam-yurt Village Jamaat||8||1.155|
|11||Tevzan Village Jamaat||8||1.155|
|12||Khatunni village Jamaat||7||1.01|
|18||Sunzha Ingush Jamaat||6||0.866|
|10||Shatoi Sector Jamaat||3||0.433|
|14||Mujahideen unit Grozny||3||0.433|
|17||Sunzha Chechen Jamaat||1||0.144|
Shamikala-Kaspisk jamaat and Jamaat Tavaakul are both extremely active jamaats, they are geospatially isolated in that particular area of Dagestan according to the data so far.
Finally, betweenness centrality was run. Betweenness centrality is used to identify nodes that lie on paths to other nodes. For example, if I were to apply for a job, the first person I would talk to would ostensibly be someone who works in human resources (if I wasn’t directed to apply online first). The person in HR would lie on a path between myself and the individual hiring me for the position. Therefore, the person in HR has a betweenness of higher value, a power given to them simply because of their position in the network because they can control the flow of information. In regards to a 2-mode network that has been conflated and binarized, the centrality is as follows:
Figure 9. Freeman Betweenness Centrality Output
|Freeman Betweenness Centrality||1||2|
|9||Shali Sector Jammat||30.247||10.959|
|11||Tevzan Village Jamaat||24.059||8.717|
|18||Sunzha Ingush Jamaat||23||8.333|
|5||Asam-yurt Village Jamaat||10.602||3.841|
|12||Khatunni village Jamaat||4.104||1.487|
|10||Shatoi Sector Jamaat||3.9||1.413|
|14||Mujahideen unit Grozny||1.733||0.628|
|17||Sunzha Chechen Jamaat||0||0|
|DESCRIPTIVE STATISTICS FOR EACH MEASURE|
|10||N of Obs||25||25|
|Network Centralization Index||0.3889|
With a network centralization index at 0.3889, we can concur that there is a moderate distribution of betweenness value per jamaat within the network. This shows us that the majority of jamaats do not lie on paths between other jamaats, another highlight to the localization of each jamaat. Because there are no short graph theoretic distances between the jamaats, these distances may give the jamaats antagonisms and allows specifically esoteric ways of thinking. In essence, because these jamaats are concerned with local matters, their thinking may not be synchronized with CE leadership even if they all adhere to the same Islamist-Jihadist theo-ideology. Immediacy trumps ideology in this picture and this should be taken into account when analyzing the violence that is attributed to the CE network.
Figure 10. Metric Multi Dimensional Scaling
Figure 10 displays a metric multidimensional scaling visualization of similarities between groups
and events. Metric multidimensional scaling is used to determine similarity and dissimilarity between data. The points are arranged so that distances between points resemble relations that are similar. So, long distances resemble dissimilarities and short distances resemble similarities. From the data above, Achkoy-Martanovsky and Urus-Martanovsky have exact MDS coordinates resembling high similarity. Since these two groups operate in heavily overlapped areas of operation, this is logical. The Chechen-Sunzha jammat differs from the Ingush-Sunzha jamaat slightly.
Conclusions and Considerations
The Caucasus Emirate is a dark network and one of the darkest in nature. Relational data is extremely hard to come by through open-source methods. The few individuals who have escaped the Northern Caucasus with information about the CE have done so at the risk of their lives. Given the analyses that have been undertaken in this paper, the hope that additional information will surface and allow researchers to perform in-depth critiques is great. One thing that can be garnered from this paper is the need for alternative approaches to disrupting and destabilizing the CE. Decentralization of the network may possibly play the largest role in how it should be approached. Leaders at central CE leadership, vilaiyat level, sector level, and jamaat level live only a couple of years at most on average yet this network continues to flourish and violence has increased annually since 2007 (excluding 2010 to 2011).
With the Sochi Olympics rapidly approaching in 2014, not only the Russians but also the global community should be concerned with the amount of violence occurring in the area. If serious advances are not made in regards to quelling violence and stabilizing the republics, the CE leadership will without a doubt have a much easier time in recruiting talent, operating in destabilized areas as well as exporting violence westward and into greater Russia. Although this paper has focused on the localization of violence, it should be noted that this violence is easily exported. A successful mass-casualty attack would augment the global-jihad movement and present a new shift in capabilities. The second and third order effects are difficult to establish but it would be easy to surmise that the CE as well as allied organizations such as Al Qa`ida, the Taliban, the Islamic Movement of Uzbekistan, and Islamic Jihad Union, among others, would be invigorated. Since there were 28 jamaats available for this study, there are potentially at least 28 vectors for the exportation of extremist violence, and this does not include the possible permutations created through the collaboration of jamaats and spcialized Caucasus Emirate’s units lile the Riyadus-Salkhiin Martyrs Brigade of suicide bombers and their handlers.
 Gordon Hahn. Russia’s Islamic Threat, Yale University Press, 2007, p.23.
 See Appendix 1 for census data for Dagestan.
 Robert W. Schaefer. The Insurgency in Chechnya and the North Caucasus: From Gazavat to Jihad, Praeger Security International, 2011,p.73
 See Appendix 1.
 Kisreiv, p.102. Tensions did not subside as sharp politicization in the village continued in the village between Sufis and Wahhabis through 1998 and 1999, leading to the August 1999 invasion of Dagestan led by Khattab, Al Qa`ida’s amir in the North Caucasus, and his close associate Shamil Basaev, who served as Deputy prime Minister and then Prime Minister of the Chechen Republic of Ichkeriya from 1997 to July 1998.
 Kisriev, p.104.
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 Mark Sageman studied 172 operatives involved in the global Salafi jihad.
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|Republic Dagestan (2002.)|
|All||Avars a||Dargin. 2||Kumyk.||Lezgins.||Laks||Tabas .||Aguls||Rutul .||Tsakhur.||Nogay .||Russian||Chechens .||Azeri||e.eg.t. 3|
|Dagestan, the Lights||26,346||30||2.197||203||5,860||47||9,359||922||86||15||3||505||3||6,917||15|
|Mr. South Suhokumsk||9,777||4,683||1.812||371||1.046||877||46||74||35||32||58||531||34||52||19|
|Mountains . and dministratsiya||-100%||-28.40%||-15.00%||-39.20%||-3.40%||-4.40%||-1.00%||-0.60%||-0.50%||-0.50%||-4.50%||-1.80%||-0.10%||-0.30%||-0.10%|
|Mountains . and dministratsiya||-100%||-70.30%||-2.30%||-12.70%||-2.30%||-5.40%||-0.30%||-0.10%||-0.20%||-0.10%||-0.10%||-4.20%||-1.00%||-0.20%||-0.10%|
|Suleiman Stalskiy District||54,036||4||9||6||53,824||7||34||8||2||–||–||100||3||8||–|
|A. with Ando-tsez people and archintsami; proper distribution of the Avars, Ando-Tsez archintsev peoples of Dagestan on the ATE, see the link|
|Two. with Kubachins and kaytagtsami ; proper distribution Dargins, Kubachins and kaytagtsev for ATE Dagestan, see the link|
GORDON M. HAHN’S RECENT PUBLICATIONS AND INTERVIEWS
Gordon M. Hahn, “The Caucasus Emirate Jihadists: The Security and Strategic Implications,” in Stephen J. Blank, ed., Russia’s Homegrown Insurgency: Jihad in the North Caucasus (Carlisle Barracks, PA: U.S. Army War College Strategic Studies Institute, October 2012), pp. 1-98, http://www.strategicstudiesinstitute.army.mil/pdffiles/PUB1116.pdf.
Gordon M. Hahn, “More Russian Election Intrigue,” Russia – Other Points of View, 24 October 2012, http://www.russiaotherpointsofview.com/2012/10/more-russian-election-intrigue.html.
Gordon M. Hahn, “Tandemology 2.0: Putin’s Return, Medvedev’s Decline, and Tandem Spats,” Russia – Other Points of View, 7 October 2012, http://www.russiaotherpointsofview.com/2012/10/tandemology-20-putins-return-medvedevs-decline-and-tandem-spats.html.
Gordon M. Hahn, “Response to Estonian Defence Minister’s Erroneous Article: Problems of Expansionism: Russia, NATO, and Georgia,” Russia – Other Points of View, 29 September 2012, http://www.russiaotherpointsofview.com/2012/09/response-to-estonian-defense-ministers-erroneous-article.html.
Gordon M. Hahn, “Make It Stop: Another MSM Caricature of Russia” (Critique of David Satter in The National Review), Russia – Other Points of View, 18 September 2012, http://www.russiaotherpointsofview.com/2012/09/make-it-stop-another-msm-caricature-of-russia.html.
Interview excerpts in Defence Report article “Russia’s homegrown North Caucasus insurgency pushes for Islamist state” by Marion Ghibaudo. See Defence Report, 20 September 2012, http://defencereport.com/russias-homegrown-north-caucasus-insurgency-pushes-for-islamist-state/.
Islam, Islamism and Politics in Eurasia Report (IIPER) is a project of the Russia and Eurasia Program at the Center for Strategic and International Studies. It focuses on all politically-relevant issues involving or bearing on Islam, Islamism, and Jihadism in Russia and Eurasia writ large. All issues of IIPER will soon be permanently archived at http://csis.org/program/russia-and-eurasia-program. All back issues temporarily remain archived at: http://www.miis.edu/academics/faculty/ghahn/report.
IIPER is compiled, edited and, unless indicated otherwise, written by Dr. Gordon M. Hahn. Dr. Hahn is a Senior Associate (Non-Resident) in the Russia and Eurasia Program at the Center for Strategic and International Studies, Washington, D.C., Senior Researcher and Adjunct Professor at the Monterey Terrorism Research and Education Program (MonTREP), Monterey, California. He is also a Senior Researcher at the Center for Terrorism and Intelligence Studies (CETIS), Akribis Group and an Analyst and Consultant for Russia Other Points of View – Russia Media Watch, http://www.russiaotherpointsofview.com. He teaches courses on both politics and terrorism in Russia and Eurasia at MonTREP. Dr. Hahn is the author of two well-received books, Russia’s Islamic Threat (Yale University Press, 2007) and Russia’s Revolution From Above (Transaction, 2002) as well as numerous articles on Russian, Eurasian and international politics.
IIPER welcomes submissions on any aspect of Islamic, Islamist, or Jihadist politics in Eurasia as well as financial contributions to support the project. For related inquiries or to request to be included on IIPER’s mailing list, please contact:
Dr. Gordon M. Hahn
Tel: (831) 647-3535 Fax: (831) 647-6522
Email: firstname.lastname@example.org or email@example.com