University of Southern Denmark Political fragmentation and alliances among armed non-state actors in North and Western Africa (1997-2014) Walther, Olivier; Leuprecht, Christian; Skillicorn, David Published in: Terrorism and Political Violence DOI: 10.1080/09546553.2017.1364635 Publication date: 2020 Document version: Accepted manuscript Citation for pulished version (APA): Walther, O., Leuprecht, C., & Skillicorn, D. (2020). Political fragmentation and alliances among armed non-state actors in North and Western Africa (1997-2014). Terrorism and Political Violence, 32(1), 167-186. https://doi.org/10.1080/09546553.2017.1364635 Go to publication entry in University of Southern Denmark's Research Portal Terms of use This work is brought to you by the University of Southern Denmark. Unless otherwise specified it has been shared according to the terms for self-archiving. If no other license is stated, these terms apply: • You may download this work for personal use only. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying this open access version If you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [email protected]Download date: 28. Jul. 2022
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University of Southern Denmark
Political fragmentation and alliances among armed non-state actors in North and WesternAfrica (1997-2014)
Walther, Olivier; Leuprecht, Christian; Skillicorn, David
Published in:Terrorism and Political Violence
DOI:10.1080/09546553.2017.1364635
Publication date:2020
Document version:Accepted manuscript
Citation for pulished version (APA):Walther, O., Leuprecht, C., & Skillicorn, D. (2020). Political fragmentation and alliances among armed non-stateactors in North and Western Africa (1997-2014). Terrorism and Political Violence, 32(1), 167-186.https://doi.org/10.1080/09546553.2017.1364635
Go to publication entry in University of Southern Denmark's Research Portal
Terms of useThis work is brought to you by the University of Southern Denmark.Unless otherwise specified it has been shared according to the terms for self-archiving.If no other license is stated, these terms apply:
• You may download this work for personal use only. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying this open access versionIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim.Please direct all enquiries to [email protected]
Political Fragmentation and Alliances among Armed Non-State Actors in North and
Western Africa (1997-2014)
Olivier Walther a,b, Christian Leuprecht c,e and David B. Skillicorn d aUniversity of Florida, Center for African Studies, Gainesville, Florida; bUniversity of Southern
Denmark, Department of Political Science, Sønderborg, Denmark; cRoyal Military College of
Canada, Political Science, Kingston, Ontario, Canada; dQueen’s University, School of
Computing, Kingston, Ontario, Canada; eCollege of Business, Government & Law, Flinders
University of South Australia
TERRORISM AND POLITICAL VIOLENCE
2020, VOL. 32, NO. 1, 167–186
https://doi.org/10.1080/09546553.2017.1364635
Abstract
Drawing on a collection of open source data, the article uses network analysis to represent
alliances and conflicts between 179 organizations involved in violence in North and Western
Africa between 1997 and 2014. Owing to the fundamentally relational nature of internecine
violence, this article investigates the way the structural positions of conflicting parties affect their
ability to resort to political violence. To this end, we combine two spectral embedding techniques
that have previously been considered separately: one for directed graphs that takes into account
the direction of relationships between belligerents, and one for signed graphs that takes into
consideration whether relationships between groups are positive or negative. We hypothesize
that groups with similar allies and foes have similar patterns of aggression. In a region where
alliances are fluid and actors often change sides, the propensity to use political violence
correspond to a group’s position in the social network.
Introduction
In a recent letter addressed to the President of the Islamic Council of Mali on 27 September
2016, Iyad ag Ghaly, the leader of the jihadist group Ansar Dine, announced that he would
unilaterally cease attacks throughout Mali “and especially in the North of the country”. Signed
This is an Accepted Manuscript of an article published by Taylor & Francis in Terrorism and Political Violence on September 26th, 2017, available online: http://www.tandfonline.com/10.1080/09546553.2017.1364635
2
on behalf of “Ansar Dine and its allies”, the letter further explained that the group would not
renounce its goal of imposing Islamic law (sharia) but would work towards a ceasefire to “ensure
the security of persons and their property and promote social cohesion, a guarantee of peace and
stability”1.
The letter arrived one month before Ansar Dine attacked a UN convoy in the north of the country
(RFI 2016). The subject of much debate, this is the latest development in a tortuous military
career for ag Ghaly, who, since the 1990s, has been a mercenary for the late Col. Gaddafi, rebel,
negotiator for the Malian government, consular officer in Saudi Arabia, leader of a terrorist
group, and fugitive. The fact that a militant such as him has successively worked for and against
the state, within Mali and abroad, and as a civilian and a military leader, illustrates just how fluid
many modern African conflicts are: commanders and rank-and-file fighters frequently shift
allegiances among regular forces and armed non-state actors. A similar volatility characterizes
political allegiances between governments and myriad often ephemeral armed groups, who split
and coalesce as new opportunities arise. While groups that appear at odds one day may be allies
the next, splinter groups formed after leaders fall out with one another might nonetheless
collaborate against a third party.
The complex motivations and outcomes of such alliances and conflicts have received growing
attention over the last decade2. On the one hand, a number of detailed qualitative studies have
contributed to documenting how relationships among rebels, religious extremists and traffickers
that developed in North and Western Africa were mainly based on corruption around illegal
flows of drugs, weapons and migrants3 and had fundamentally changed the political landscape of
the region4. Mali, with its many short-lived alliances between secessionist and Islamist groups
with conflicting agendas, has been of particular interest5. On the other hand, a growing body of
quantitative studies identifies internal fragmentation, conflicts and alliances between armed
groups as a crucial explanation for the onset and diffusion of internecine violence6 and often
elusive quest for peace settlements7.
This article bridges these strands of literature through a more formal approach to social networks
of belligerents in the region. Examining the relationships between alliances and conflicts as a
3
putative explanation for the patterns of violence in North and Western Africa, the article posits a
relational approach to the study of the structure of relationships among state and non-state actors.
In doing so, it builds on a growing body of literature that takes advantage of the recent
availability of disaggregated data to map and model ties between and within violent
organizations8.
The article proceeds as follows. The second section reviews the literature on conflict and signed
networks and shows that greater access to geo-referenced data and the use of spatial statistical
analysis has advanced the study of patterns of armed groups over the past decade. The third
section presents the data and explains how we structured them into networks of belligerents. The
fourth section models the structural position of actors in conflict. The last section addresses the
implications of the findings for theory, method, and practice.
Previous Research on Conflicts and Signed Networks
While past analyses of (civil) wars were limited by a lack of reliable data, the proliferation of
satellite and disaggregated data has spawned innovative approaches to investigating the onset
and diffusion of political violence across time and space 9. The concomitant proliferation of
political and economic predictors, on which the spatial-analytical approach can draw, now
includes factors as diverse as the nature of government, ethnic divisions, poverty, income,
inequality, number and morale of troops, frequency of droughts, and endowment of natural
resources10.
Some factors that may explain why groups resort to violence are also related to the structure of
relationships that connect actors in conflict11. Modern African conflicts bring together a
multitude of state and non-state belligerents that include regular military forces, pro-government,
ethnic and religious militias, rebels, secessionist and self-determination movements, violent
Islamist groups, warlords, thugs and criminals12. The relationships within and between these
actors are often characterized by a bewildering array of alliances and conflicts13. In North and
Western Africa, for example, the Salafist Group for Preach and Combat (GSPC) – a splinter
group of the Algerian Armed Islamic Group – rebranded itself as AQIM in 2007. Some of its
members broke off in 2011 to form MUJAO while others formed Al Moulathamoun and Al
4
Mouakaoune Biddam. In 2013, MUJAO merged with Al Moulathamoun to form Al
Mourabitoune, which, in 2015, was renamed Al Qaeda in West Africa. More recently, AQIM,
Ansar Dine, Al Mourabitoun and the Macina Liberation Front merged to form the “Group for the
Support of Islam and Muslims” (Jama’at Nusrat al-Islam wal-Muslimin)14. These mergers, splits
and name change suggest that organizations affiliated with Al Qaeda share a common historical
and ideological background and form several components of a single, opportunistic network,
rather than independent entities. Causes and consequences of the patterns of violence associated
with such alliances and conflicts have received increasing attention over recent years15.
Research focusing on intragroup dynamics suggests that the internal structure of warring factions
is central to explaining patterns of violence of non-state actors, be they insurgents16 or
terrorists17. Social ties forged before and during war between belligerents make violent
organizations more cohesive, less prone to factionalization, and facilitate recruitment and
allegiance during conflicts. Internal divisions in self-determination movements are associated
with a greater probability of civil wars because the multiplication of belligerents creates political
uncertainties as to what concessions could be made and what commitments could resolve a
conflict through non-violent means18. Internally divided self-determination movements are also
more likely to receive concessions than unitary ones because states often “divide and concede”
rather than “divide and conquer”19. While fragmented groups seem to increase the intensity of
violence, particularly against civilians20, the effect of the fragmentation of violent groups on the
duration of conflicts remains controversial. Some studies suggest that fragmentation complicates
peace settlements by multiplying the number of ‘veto players’ that must approve a settlement21.
Others argue that fragmentation accelerates them by weakening belligerents and forcing them to
cooperate22.
Studies focusing on intergroup dynamics suggest that violence between non-state actors can be
understood as a mean for access to resources and political leverage to fight central
governments23. This explains why rebel groups often fight each other instead of forming
coalitions24, particularly when the government lacks repressive power25. Research on armed
conflict between non-state actors shows that inter-rebel violence is more likely in drug
production areas, where rebel groups have established control over territory beyond the
5
government’s reach and are numerically strong, and where states are unable to exercise their
authority26. That intergroup alliances also shape the outcome of civil wars is less documented.
While intergroup alliances rarely lead to victory, interdependencies between rebel groups bring
valuable resources such as intelligence and tactical support that can be used against a well-
organized and capable government to avoid defeat27. In conflict situations where an external
party, such as a foreign military power, can enforce cooperation between warring parties that
leads to a peace settlement, armed groups might have an interest in forming coalitions and
aligning with the side they believe to have the greatest chance of emerging victorious28.
Recent studies on fragmentation and alliances among state and non-state actors approach
violence as a relational process whose structure enables and constrains action29. Other network
analysis has already observed that social actors who wish to reduce their structural constraints
can develop network tactics to alter the structure - rather than the behavior of others -- to their
advantage30.The ready availability of disaggregated data, combined with recent conceptual and
computational advances in network analysis has allowed a growing number of studies to test
such assumptions empirically using social network analysis (SNA). SNA is the study of
individual actors, groups, organizations or countries, represented by the nodes of the network,
and the relationships between these actors, represented by their links. As both a paradigm of
social interactions based on graph theory and a method, SNA seeks to understand networks by
mapping out the ties between nodes as they are rather than how they ought to be or are expected
to be31.
SNA is particularly adept at capturing the complexity of conflict situations due to its ability to
describe, represent, and model signed networks, i.e. networks that contain both positive and
negative relations. Positive ties develop to overcome collective-action problems, enforce trust
and ideology, coordinate activities at a distance, distribute resources, or disseminate ideas and
decisions. Alliances between states are typical of positive-tie networks. By contrast, negative ties
develop among actors that dislike, avoid, or fight one another. For positive and negative ties,
SNA can be used to study the structure and function of the network as a whole, and the role of
each node in the group in relation to others. Network approaches have been used to verify
whether states with common enemies have fought one another,32 how alliances or rivalries
6
between states could explain the diffusion of World War I on a global scale and to illustrate the
increasing number of alliances between African states since the end of the Cold War.33
Networks with positive ties are known to be structured differently from those with negative
ties34. Networks based on friendship, alliance and collaboration are denser and more clustered
around actors that share similar values than networks with negative ties, because individuals and
organizations tend to have more friends than enemies35. Positive-tie networks also harness more
resources, ideas, and knowledge than negative-tie networks since the latter are driven by hatred,
avoidance or conflict. As a result, many centrality measures based on the assumption that social
networks serve as conduits for flows of information, advice, or influence, such as betweenness or
closeness centrality, are unrealistic in the case of actors in conflict36. Networks with negative ties
are also well known for their low level of transitivity, a principle that assumes that two actors
that share a connection to a third actor are likely to be connected themselves.
A growing literature suggests that, despite their differences, positive- and negative-tie networks
should be analyzed simultaneously37. One way to incorporate both allies and adversaries is to use
structural balance theory, which argues that social relations are stable if they contain an even
number of negative ties. Stable groups of three actors (known as triads) are theoretically stable if
everyone likes everyone else, or if two actors are in conflict with a third party38. Over time,
unstable triads theoretically evolve towards stable triads, because instability creates tensions that
can only be resolved by altering views, behaviors and alliances. Another approach to signed
networks is to model the structural autonomy and constraints of actors. Smith et al. (2014) argue
that an actor’s political independence is constrained both by its potential to reach other actors’
resources and by the structural position of allies and enemies39. Being connected to a single ally
that is not under threat considerably reduces the autonomy of actors in signed networks, while a
diversified network of allies enhances autonomy.
This article adopts a complementary approach. Instead of assuming that political violence is
explained by attributes of the belligerents or by exogenous factors, we propose that the
propensity to use political violence corresponds to a group’s position in the social network rather
than their actions per se. To this end, the initial part of our analysis aims at representing how
7
armed non-state actors are connected to their allies and enemies. We use centrality measures to
identify subclusters of actors where conflict or cooperation is particularly developed, and
highlight the main structural differences between positive- and negative-tie networks. Since
enemies and allies are inextricably linked in real-life networks, the subsequent analytical part of
the article considers positive and negative ties simultaneously. Spectral embedding techniques
make it possible to place the nodes that represent organizations at the position that best balances
the “pull” of allies against the “push” of enemies. This makes it possible to model the balance
between the relative effects of having allies and foes simultaneously. We also take into account
the fundamentally asymmetric nature of conflicts and consider whether groups attack more or
less than they are attacked. Combining signed and directed networks, we expect groups with
similar allies and foes and similar patterns of aggression to form clusters that correspond to their
structural position in the social network.
Research Design
Our analysis relies on data from the Armed Conflict Location and Event Dataset. ACLED
provides a comprehensive list of political events by country between 1997 and 201440. The fifth
version of the data was used to select 37 armed non-state actors in 21 North and Western Africa
countries41, their allies and their enemies, excluding non-identified Islamist and Libyan militias
(see Appendix 1). The scope was limited to events with the following seven referents: Battle –
no change of territory; Battle – Non-state actor overtakes territory; Battle – Government regains
territory; Riots and protests; Violence against civilians; and Remote violence. This generated a
list of 3231 events comprised of 179 organizations and 27,791 fatalities.
The ACLED dataset describes (up to) four groups in each incident: an attacker (A), a
collaborator in the attack (B), a target (C), and a potentially assisting group that may also be a
secondary target (D). This data is used to build a social network in which the nodes are groups,
with positively weighted directed ties between allies (B to A, and D to C) and negatively
weighted directed ties between adversaries (B to C). For example, on January 12, 2014, clashes
between French troops (A) and Malian troops (B) on the one hand, and Ansar Dine (C) and
MUJAO (D) on the other hand, claimed 11 lives, including Islamist leader Abdel Krim, and left
60 injured (ACLED incident 486MLI). Incidents are aggregated so that the ties between any pair
8
of groups reflect all of their interactions. Ties can be both positive and negative, and in both
directions, between the same two groups. Direction is a proxy for intentionality: a group on the
offensive makes a conscious decision to attack while the defender has no choice, and other
groups must decide whether to join. These decisions reflect a calculus of advantage or
ideological alignment.
The resulting graph is analyzed in two steps. First, we map the networks containing negative and
positive ties separately and analyze the most prominent actors using several centrality measures.
Because negative-tie networks do not serve as conduits for flows of information, advice, or
influence, we use degree centrality, which simply refers to the standardized number of ties each
node has, and eigenvector centrality, which refers to the number of nodes adjacent to a given
node, weighted by centrality, and indicate whether nodes are connected to other well-connected
nodes. For our positive-tie network, we use eigenvector centrality and betweenness centrality,
which measures the number of shortest paths from all nodes to all others that pass through that
node42.
Second, we combine both positive and negative ties into a single network, and embed this
network in a geometric space in such a way that the distance between each pair of points
accurately reflects the balance between the ‘pull’ from collaborating groups and the ‘push’ from
aggression between them. These distances are globally integrated as a function of immediate
neighbors (i.e. actors who cooperate to fight each other) as well as neighbors of neighbors and, in
fact, the structure of the entire graph. This integration makes the process challenging: positive
relationships are naturally transitive (“the ally of my ally could plausibly become my ally”) but
negative relationships are not (the proverbial “enemy of my enemy is my friend” does not
necessarily obtain). The adjacency matrices that describe positive and negative ties combine both
kinds of ties. The representation is then normalized so that well-connected nodes are central and
poorly connected nodes peripheral43. This Laplacian matrix is used to embed the graph in a
geometry where position is meaningful (well-connected nodes are placed centrally), and
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