political fragmentation and alliances among armed non
TRANSCRIPT
![Page 1: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/1.jpg)
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]
Download date: 28. Jul. 2022
![Page 2: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/2.jpg)
1
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
![Page 3: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/3.jpg)
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
![Page 4: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/4.jpg)
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
![Page 5: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/5.jpg)
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
![Page 6: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/6.jpg)
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
![Page 7: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/7.jpg)
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
![Page 8: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/8.jpg)
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
![Page 9: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/9.jpg)
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
proximity represents similarity (similar nodes cluster together). Sets of ‘bad’ armed non-state
actors and (supposedly) ‘good’ governmental forces and civil society tend to form polar
![Page 10: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/10.jpg)
9
opposites in some dimension(s) of the representation. Since proximity represents similarity – and
alliance – distance tends to represent opposition.
A Social Network Analysis of Political Violence
Negative- and positive-tie networks
We start with a graph that represents each organization as a node that is connected to those actors
with which it is in conflict. The size of the nodes in Figure 1 is proportional to the number of ties
(or degree).
Three main clusters emerge: the Nigerian cluster that is polarized by Boko Haram; the Trans-
Saharan cluster that is composed of groups affiliated with Al Qaeda such as GSPC and AQIM
and their enemies; and the Libyan cluster that is composed of myriad Islamist brigades and pro-
government forces. With a density of only 0.023, the network is very sparse, which is typical of
networks that are made up exclusively of negative ties: the number of enemies a group can have
is often more limited than the number of potential allies44. The network also has a low level of
transitivity: in only 1.2% of the triads enemies of enemies are in fact enemies, while in most
cases (98.8%), enemies of enemies are friends. By contrast, recent studies in Syria show that
12% of the triads are intransitive, either because two enemies were opposed to each other or
because friends of friends were actually in conflict, which fuels the political and spatial diffusion
of the Syrian conflict45. Finally, organizations with adverse attributes tend to be in conflict with
one other, a tendency known as heterophily. This can be tested using the E/I index, which
calculates the difference between external and internal ties for each group of actors (government,
rebels, militias, civilians, Islamists, and external forces), divided by the total number of ties. The
E/I index for the network is positive (0.899) and statistically significant (chances of getting the
result right by guessing are less than 1%), which confirms that armed non-state actors clash with
organizations that are not in the same category.
![Page 11: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/11.jpg)
10
Figure 1: Negative ties between organizations involved in violent events, 1997-2014
Note: Isolates are not shown.
In terms of adversaries and victims (Table 1), the bloodiest conflicts have seen civilians in
conflict with state and non-state actors. Boko Haram is by far the bloodiest armed group in the
region: it kills both Nigerian civilians (6409 victims) and military forces (5447) en masse. In
Nigeria, conflicts with unidentified groups (3556 victims), Fulani militias (2446), and the
military (2382) also claimed many civilian victims. Clashes involving the Algerian GIA and
Algerian civilians were particularly deadly in the late 1990s, with 6212 victims reported in the
database. The campaigns of civilian massacres adopted by GIA explain why some of its former
members, such as Hassan Hattab, defected to form GSPC in 1998. Finally, the National
Liberation Army and the Libyan Armed Forces clashed during the Libyan civil war in 2011
![Page 12: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/12.jpg)
11
(1740 victims). More than 1350 victims are also reported as a consequence of NATO military
intervention, mostly civilians. Generally speaking, these figures confirm earlier studies: most
victims of African conflicts were civilians who either died at the hands of state or non-state
armed groups, or from the effects of displacement, malnutrition and disease46.
Table 1. Bloodiest conflicts between actors, 1997-2014
Actor 1 Actor 2 Fatalities
Civilians (Nigeria) Boko Haram 6409
Civilians (Algeria) GIA Armed Islamic Group 6212
Boko Haram Military forces of Nigeria 5447
Civilians (Nigeria) Unidentified armed group (Nigeria) 3556
Civilians (Nigeria) Fulani Ethnic Militia (Nigeria) 2446
Civilians (Nigeria) Military forces of Nigeria 2382
NLA National Liberation Army (Libya) Military forces of Libya 1740
Christian Militias (Nigeria) Muslim militia (Nigeria) 1739
Civilians (Libya) NATO forces 1367
AQIM Military forces of Algeria 1074
Military forces of Cameroon Boko Haram 1005
Source: ACLED. Note: a conflict can result from several events. Only conflicts with more than
1000 fatalities are listed.
As expected, the network is composed of few highly central organizations (Table 2) since being
in conflict with many adversaries simultaneously is widely regarded as a liability rather than an
asset47. Among armed non-state actors, AQIM scores highest on degree and eigenvector
centrality, which indicates that it has the greatest number of enemies and is connected to other
actors that also have many enemies, such as the military and police forces of Algeria. This is an
interesting result: if it is a pure liability to have many enemies, then having enemies that are
themselves involved in many conflicts offers more autonomy to AQIM. The ideal structural
situation for an actor embedded in a signed network is to have enemies that are constrained by
numerous threats that affect the outcomes of military operations, reduce their ability to
coordinate activities across the region, and limit their ability to cooperate to achieve their
![Page 13: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/13.jpg)
12
political or religious goals48. MUJAO, GSPC and GIA also occupy a prominent structural
position due to their conflicts with civilians and armed forces in several countries. Other
prominent actors include Boko Haram, which stands out for being connected to many other
actors who themselves have few connections to one other, and Libyan groups such as Ansar al-
Sharia and Libya Shield Brigade.
Table 2: Top-scoring nodes for selected centrality measures – negative ties
Rank Degree centrality Eigenvector centrality
1 AQIM (0.264) AQIM (0.743)
2 Boko Haram (0.200) MUJAO (0.421)
3 MUJAO (0.136) Military Forces of Algeria (0.289)
4 Ansar al-Sharia (0.120) GSPC (0.257)
5 Ansar Dine (0.096) Ansar Dine (0.229)
Mean 0.024 0.071
Std. Dev. 0.035 0.095
Source: ACLED. Note: Scores are indicated in brackets.
The structure of the network of enemies contrasts starkly with the one showing how
organizations involved in violent events have collaborated across the region. As depicted in
Figure 2, the positive-tie network is divided into three main unconnected groups of allies, one
triad that connects an unidentified armed group to Boko Haram and Ansaru, and three dyads.
The main cluster on the left is structured around North and West African military and police
forces and their civilian allies, which are represented in red and yellow respectively. This cluster
is indirectly connected to some of the main Islamist groups in the region, which are represented
in green, through the secessionist movement MNLA. MNLA resulted from the fusion of a
peaceful organization that was defending the rights of the local Tuareg population, an armed
group involved in several rebellions, an organization inspired by the Salafist ideology, and
Tuareg mercenaries who had formerly been employed in Libya49. MNLA was initially allied
with Ansar Dine before switching sides and fighting alongside the French-led military forces in
2013. The two other clusters are structured around the armed forces of Libya and their pro-
![Page 14: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/14.jpg)
13
government brigades and battalions, the other around Islamist groups and ethnic and communal
militias in Libya. Each cluster has a chain-like structure in which organizations are rather distant
from one another. The Algerian Private Security Forces, for example, are eight steps away from
Al Mourabitoune.
The long path-length distance, low density (0.034) and low clustering coefficient (0.104) of the
network are typical of a structure that is not organized around groups of tightly connected actors.
This suggests that most governmental forces and armed non-state actors tend to build bilateral or
trilateral alliances rather than broad coalitions across the region. The graph also highlights the
lack of regional cooperation between government forces that face similar threats: there is no
reported tie between the military forces of Libya and Algeria, or between the military forces of
Cameroon and Nigeria.
Figure 2: Positive ties between organizations involved in violent events, 1997-2014
![Page 15: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/15.jpg)
14
Source: ACLED. Notes: green nodes refer to Islamist groups, red to government forces, yellow
to civilians, and blue to other actors.
Military and police forces have the highest eigenvector and betweenness centrality, followed by
Ansar al-Sharia and the Shura Council of Benghazi Revolutionaries (BSCR), both of which hail
from Libya (Table 3). Generally speaking, betweenness centrality scores – indicating the
propensity to bridge clusters – are very low, even for top-scoring nodes, which suggests that the
networks contain few exceptional brokers. Only the French military forces play a role in bridging
several African armed forces that would otherwise not be connected, hence their high
betweenness centrality. Once again, the isolation of Boko Haram in Nigeria contrasts sharply
with the network of alliances among other Sahelo-Saharan and Libyan groups.
Table 3: Top-scoring nodes for selected centrality measures – positive ties
Rank Eigenvector centrality Betweenness centrality
1 Military Forces of Nigeria (0.400) Military Forces of France (0.111)
2 Police Forces of Nigeria (0.379) Military Forces of Algeria (0.071)
3 Ansar al-Sharia (0.223) Military Forces of Mali (0.070)
4 Shura Council of Benghazi Revolutionaries
(0.260)
Military Forces of Nigeria (0.062)
5 Military Forces of Libya (0.193) MNLA (0.052)
Mean 0.039 0.012
St. Dev. 0.082 0.022
Source: ACLED. Calculations by the authors. Note: Scores are indicated in brackets.
Spectral embedding
Spectral embedding computes a representation of a graph with edge weights (representing tie
strength) by projecting it into a low-dimensional space in such a way that nodes that are similar
(have many, or strong, edges between them) are placed close to one another. As a consequence,
nodes that are important in the network tend to be embedded close to the center. A spectral
embedding may not be visually clear, in the sense that Figure 2 is, but it is guaranteed,
![Page 16: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/16.jpg)
15
mathematically, to be as accurate as possible in the given dimensionality. The networks we
derive from the ACLED data require extensions of the techniques of spectral embedding because
their edges are directed, and they have both positive and negative weights.50 Once again the
resulting embeddings are guaranteed to be the most accurate possible in the given
dimensionality, given assumptions about the relative importance and positive versus negative
ties. Spectral embeddings are intrinsically inductive techniques; they do not require analysts to
hypothesize variables that might cause ties to form in particular contexts; rather they take data
about which ties actually did form, construct the resulting global social network, and
demonstrate the structure of this network, leaving the analyst to infer plausible explanations from
the structure.
To compute spectral embeddings of the social networks derived from the ACLED data (shown in
Figure 3), initially, for the sake of simplicity, we disregard the direction of the ties. Negative ties
resulting from recorded attacks are shown in red and positive ties resulting from alliances, or at
least common purpose, are shown in green. The general structure is polar opposites that represent
groups whose primary relationship is that they attack or are attacked by groups at the other
extreme. The graph clearly shows how ‘bad’ actors such as Islamist and Jihadist groups are
grouped opposite ‘good’ actors, both violent and non-violent. The contrast is particularly evident
for Boko Haram, and its opposition to governmental forces and civilians from Nigeria and
Cameroon, as well as for GIA-GSPC-AQIM, and its opposition to Algerian armed forces and
civilians. The graph also shows that the attack patterns of GIA, GSPC and AQMI differ
markedly from those of Ansar Dine, MUJAO and Al Mourabitoune, which are located much
closer to the center of Figure 3.
![Page 17: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/17.jpg)
16
Figure 3: Spectral embedding showing positive and negative ties
Any measure that considers a group in isolation is unable to distinguish armed non-state actors
from military or police organizations because both have similar patterns of interaction. We,
therefore, compute measures of outward and inward aggression based not on the number of such
incidents but on the length of the relevant ties in the embeddings. A group’s position in the
embedding reflects its relationships with all of the groups with which it interacts; therefore, the
length of the embedded ties is more revealing than simply the number of attacks. For example,
the distance of a group from the center of the embedding reflects not only how many other
groups attack it (or are attacked by it) but also the extent to which its enemies are similar to one
another (close in the embedding). Thus a long red tie reflects not only the existence and
frequency of attacks, but also their strategic intensity. Figure 4 plots groups at the same positions
as in the spectral embedding presented in Figure 3 but labelled to distinguish their “levels of
aggression”: the difference between the outgoing aggression that each group causes and the
![Page 18: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/18.jpg)
17
incoming aggression to which it is subjected. The points are color-coded: red means a group
generates more aggression than it receives; orange means that the group generates some outgoing
aggression; and green means that there is no outgoing aggression (individual scores are presented
in Appendix 1).
Figure 4: Spectral embedding showing levels of aggression
The vicinity of groups presented in Figure 4 allows us to distinguish armed non-state actors (red,
with almost all neighbors red as well) from national defense forces (red, but with many orange or
green neighbors). In other words, most of the polar opposites are structurally distinct. ‘Bad’ net
aggressors such as AQIM or Boko Haram tend to cluster together, or are isolated; ‘good’
aggressors such as militaries tend to cluster with orange and green groups. Neutral actors such as
the International Committee of the Red Cross (ICRC) tend to fall in the middle and are colored
green. Victims are also green but tend to be located near their champions.
![Page 19: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/19.jpg)
18
Northern Nigeria and Libya are particularly interesting as they involve many armed non-state
actors with strong structural constraints. We would expect Northern Nigeria, where Boko Haram
is particularly dominant, to have more of a dual structure than Libya, where a plethora of violent
groups compete for control of the state and oil resources. Indeed, spectral embedding showing
conflict and cooperation for 37 organizations in Northern Nigeria (Figure 5) clearly confirms that
Boko Haram is in conflict with virtually everyone, a situation comparable to that of Daesh in the
Middle East, which opposes all governments and non-state actors – including Al Qaeda – in the
region.
Figure 5: Spectral embedding showing positive Figure 6: Spectral embedding showing
and negative ties for 37 organization in positive and negative ties for 30
Northern Nigeria organizations in Libya
Note: for the sake of clarity, Ansaru is not shown.
In Libya, spectral embedding conducted on 30 organizations highlights the ongoing conflict
between weak alliances of pro-Islamist groups and weak alliances of pro-government forces
(Figure 6). Islamist groups, on the left of the graph are composed of Islamist militias such as
Libya Dawn and Libya Shield, and of Jihadist groups close to Al Qaeda such as the
Revolutionaries Shura Council (BRSC), a coalition that includes Ansar al-Sharia, the 17
February Brigade, and the Rafallah Sehati Brigade. These groups, based in Tripoli and Benghazi,
![Page 20: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/20.jpg)
19
oppose the Libyan army, as indicated by several long red ties. Among pro-government forces, on
the right, are anti-Islamist militias such as the Zintan Militia, the Al-Sawaiq Battalion and the Al
Qaqa Brigade. Civilians and journalists are located near the internationally recognized authorities
of Libya.
Conclusion
This article has illustrated the effectiveness of extending social network analysis to the structure
of armed non-state actors in North and West Africa, a region which, over the past 20 years, has
become more politically unstable. However, conventional social network measures fail in these
settings. For example, measures such as betweenness are inappropriate because negativity does
not ‘flow’ in the way that positivity is conceived, and centrality is not a crucial property when
negativity separates nodes far from the center. Instead, our methodological contribution is based
on a novel approach that combines signed and directed graphs to highlight opposed groups and
distinguish among several kinds of aggressors as a function of their conflict patterns. In settings
where groups form shifting alliances and oppositions, an approach that takes into account not
only local, pairwise relationships, but also global patterns that emerge, is needed for situational
awareness. Conventional social network analysis can represent positive ties, but not ties where
direction matters, or where ties represent a negative association. Furthermore, these are not
independent properties of a social network and so must be represented together.
In the process, the article advances theory on the fragmentation of conflict. We used open source
data to map how 179 organizations involved in political violence were structurally connected
through conflict and alliances. Our analysis shows the extent to which the network that connects
actors in conflict has a low density, a low level of transitivity, and contains few central actors,
three typical features of negative-tie networks. AQIM is unequivocally the most connected
organization, both in terms of the overall number of actors with which the group is in conflict,
and the respective centrality of its enemies. In network terms, this is a liability. Divided into
several clusters, the positive-tie network has a long path-length distance, low density and low
clustering coefficient, a structure that suggests that most organizations tend to build limited
alliances rather than broad coalitions across the region.
![Page 21: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/21.jpg)
20
We then combined the two networks and modeled the effect of having friends and foes
simultaneously. Using the attack relationships, we also measured the level of outgoing and
incoming aggression of each group. From this approach, five categories emerge: (1) neutral
actors, represented in the middle of our graphs; three kinds of groups that cluster together – (2)
victims, (3) groups that are attacked more than they themselves attack, and (4) groups that
counter violence and thus attack more than they are attacked (e.g. militaries) -- and (5) violent
extremist groups that attack more than they are attacked, such as armed non-state actors. Groups
that are net attackers are indistinguishable at the level of individual behavior, but clearly separate
into pro- and anti-violent extremism based on the groups to which they are close. This
conclusion is in line with our original proposition that the propensity to use political violence
corresponds to a group’s position in the social network rather than their actions per se. This
raises situational awareness in a setting where it may be difficult to distinguish ‘good’ from ‘bad’
actors based on their apparent goals or ideology.
These findings have policy implications for governments and external forces involved in
deterring armed non-state actors. First, unlike their adversaries, armed non-state actors are
connected across the region. In recent years, several ‘Sahel’ strategies have been initiated by
organizations as diverse as the European Union (2011), the United Nations (2013), the Economic
Community of West African States (2014), the African Union (2014), and the regional
coordination framework G5 Sahel, to improve governance, security and development in the
region. Building institutional capacity around common interests is likely to pay off in a region
that is largely devoid of collective security institutions that could help countries coordinate, build
trust, and go beyond ad hoc engagements. Precedent also suggests that states outside the region
will continue to play a supporting rather than a leading role. In addition to supporting capacity-
building efforts already underway, Western governments should be prepared to mount a
comprehensive Whole-of-Government effort in support of local authorities that will minimize
their local footprint, while optimizing outcomes. From a military perspective, the tenuous
personal allegiances in the region call for a mobile and flexible military response. Regional
volatility notwithstanding, operations Serval and Barkhane suggest that desert insurgents are not
impervious to external attack. As Western armies and their African allies become more mobile
![Page 22: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/22.jpg)
21
and flexible in their regional responses to political violence, desert insurgency proves to be a
double-edged sword that can also work against those who know the terrain best.
![Page 23: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/23.jpg)
22
Appendix 1. Violent political organizations, 1997-2014
Abu Obeida Brigade
Abu Salim Martyrs’ Brigade
Al Qaeda
Al Qaqa Brigade
Al-Burayqah Martyr’s Brigade
Al-Salafiya
Al Jihadia
Ansar al-Sharia
Ansar Dine
Ansaru
AQIM: Al Qaeda in the Islamic Maghreb
Boko Haram
Brega Martyrs Brigade
El-Farouk Brigade
Falcons for the Liberation of Africa
February 17 Martyrs Brigade
Fighters of The Martyrs Brigade
FIS: Islamic Salvation Front
GIA: Armed Islamic Group
GMA: Mourabitounes Group of Azawad
GSL: Free Salafist Group
GSPC: Salafist Group for Call and Combat
Islamic Emirate of Barqa
Islamic State of Tripoli
Knights of Change
Libya Shield Brigade
LIDD: The Islamic League for Preaching and Holy Struggle
Martyrs’ Brigade
MUJAO: Movement for Unity and Jihad in West Africa
![Page 24: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/24.jpg)
23
Muslim Brotherhood
Nawasi Brigade
Nusur al-Sahel Brigade
Rafallah Sehati Brigade
Soldiers of the Caliphate in Algeria
Those Who Signed in Blood
Timizart Brigade
![Page 25: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/25.jpg)
24
Appendix 2. Aggression levels for all groups that have more than one enemy
Group aggression out aggression in aggression Rioters (Libya) 1.32 1.32 0.00 Military Forces of Libya 0.43 0.50 0.07 MUJAO 0.32 0.84 0.53 Military Forces of Algeria 0.28 0.50 0.21 Protesters (Mali) 0.21 0.21 0.00 Protesters (Libya) 0.19 0.19 0.00 GIA 0.14 0.31 0.17 Military Forces of Tunisia 0.14 0.14 0.00 Police Forces of Tunisia 0.13 0.13 0.00 Unidentified Armed Group (Algeria) 0.11 0.11 0.00 Civilians (France) 0.11 0.26 0.15 GMA 0.10 0.16 0.06 Libya Shield Brigade 0.09 0.54 0.45 Military Forces of Chad 0.09 0.12 0.04 Military Forces of Libya Special Forces 0.06 0.13 0.06 Libyan Rebel Forces 0.06 0.27 0.20 LIDD 0.06 0.09 0.03 GSPC 0.05 0.31 0.26 AQIM 0.05 0.76 0.71 Military Forces of Nigeria 0.04 0.09 0.05 Wershefana Communal Militia (Libya) 0.03 0.14 0.10 GLD 0.01 0.04 0.04 Boko Haram 0.01 0.19 0.18 Ansar Dine 0.00 0.42 0.42 El-Farouk Brigade 0.00 0.06 0.06 Military Forces of Mali 0.00 0.22 0.22 MNLA 0.00 0.23 0.23 Military Forces of France 0.00 0.25 0.25 Those Who Signed in Blood -0.01 0.09 0.09 Martyrs Brigade -0.02 0.04 0.06 February 17 Martyrs Brigade -0.02 0.16 0.18 Patriot Militia of Algerian Government -0.03 0.12 0.15 Abu Salim Martyrs Brigade -0.04 0.00 0.04 Civilians (Nigeria) -0.04 0.05 0.09 Military Forces of Mauritania -0.05 0.05 0.10 Military Forces of Niger -0.07 0.10 0.17 Al Qaqa Brigade -0.07 0.00 0.07 Ansaru -0.08 0.04 0.12
![Page 26: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/26.jpg)
25
Police Forces of Algeria -0.08 0.13 0.21 FIS -0.08 0.00 0.08 Al Qaeda -0.10 0.21 0.31 Unidentified Armed Group (Libya) -0.10 0.37 0.48 Civilians (Libya) -0.12 0.09 0.21 Police Forces of Morocco -0.12 0.00 0.12 Civilians (Niger) -0.12 0.00 0.12 Civilians (Algeria) -0.12 0.08 0.21 Rafallah Sehati Brigade -0.13 0.00 0.13 UN -0.14 0.07 0.21 Zawia Ethnic Militia (Libya) -0.16 0.00 0.16 Civilians (Morocco) -0.16 0.00 0.16 Civilians (International) -0.20 0.04 0.24 Soldiers of the Caliphate in Algeria -0.21 0.00 0.21 Civilians (Mali) -0.36 0.00 0.36 Ansar al-Sharia -0.52 0.53 1.05 Muslim Brotherhood -0.91 0.00 0.91
Note: For each group, the out aggression is the average length of outgoing attack ties in the
embedded graph, in aggression is the average length of incoming attack ties, and net aggression
is the difference of the two.
![Page 27: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/27.jpg)
26
Appendix 3. Aggression levels for Libyan organizations
Group aggression out aggression in aggression Protesters 1.24 1.24 0.00 Military Forces 0.77 0.83 0.07 Libyan Rebel Forces 0.41 0.68 0.28 Libya Shield Brigade 0.36 1.13 0.77 Wershefana Communal Militia 0.20 0.51 0.31 Misratah Communal Militia 0.00 0.32 0.32 Abu Salim Martyrs Brigade 0.00 0.06 0.06 Ansar al-Sharia -0.04 0.70 0.74 Islamist Militia -0.06 0.00 0.06 Brega Martyrs Brigade -0.06 0.00 0.06 Vigilante Militia -0.07 0.00 0.07 Al Qaeda -0.09 0.00 0.09 Al Qaqa Brigade -0.11 0.85 0.96 Zintan Ethnic Militia -0.22 0.00 0.22 Operation Libya Dawn -0.23 0.00 0.23 Zawia Ethnic Militia -0.25 0.00 0.25 Civilians -0.26 0.22 0.48 Gharyan Communal Militia -0.30 0.00 0.30 February 17 Martyrs Brigade -0.47 0.06 0.53 Rafallah Sehati Brigade -0.81 0.00 0.81
Source: ACLED. Calculations: authors. Note: the following organizations with entirely zero
rows have been removed from the table: El-Farouk Brigade, Al-Sawaiq Battalion, BSRC,
Journalists, Police Forces, Salafist Group, Mutiny of Military Forces, Janzur Communal Militia,
Awlad Suleiman Ethnic Militia, Shura Council of Benghazi Revolutionaries.
![Page 28: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/28.jpg)
27
Appendix 4. Aggression levels for Nigerian organizations
Group aggression out aggression in aggression Boko Haram 1.08 3.51 2.43 Martyrs Brigade 0.98 0.98 0.00 Ansaru 0.72 1.09 0.36 Shuwa Ethnic Militia 0.48 0.48 0.00 Lassa Communal Militia 0.47 0.47 0.00 Kawuri Communal Militia 0.42 0.42 0.00 Military Forces 0.36 0.41 0.04 Attagara Communal Militia 0.32 0.32 0.00 Civilian Joint Task Force 0.04 0.04 0.00 Borno Vigilance Youths Group 0.04 0.04 0.00 Unidentified Armed Group 0.02 0.02 0.00 Civilians (Lebanon) 0.00 0.00 0.00 Police Forces 0.00 0.04 0.04 Vigilante Militia 0.00 0.04 0.04 Militia (Ali Kwara) 0.00 0.00 0.00 Fulani Ethnic Group 0.00 0.00 0.00 All Progressives Congress 0.00 0.00 0.00 VGN: Vigilante Group 0.00 0.48 0.48 Shiite Muslim Group 0.00 0.00 0.00 Civilians (China) -0.03 0.00 0.03 UN -0.04 0.00 0.04 Christian Militia -0.31 0.00 0.31 Civilians (International) -0.57 0.00 0.57 Military Forces Joint Task Force -0.64 0.00 0.64 Civilians (South Korea) -0.70 0.00 0.70 Private Security Forces -0.78 0.00 0.78 Civilians (Europe) -0.89 0.00 0.89 Civilians -0.98 0.04 1.02
Source: ACLED. Calculations: authors. Note: the following organizations with entirely zero
rows have been removed from the table: ANPP, Prison Guards, Muslim Group, Christian Group,
Students, PDP, Government, Military Forces UK, Igbo Ethnic Group.
1 MaliActu. 2016. “Mali: Iyad Ag Ghaly (Ancardine) Souhaiterait Un Cessez Le Feu,” last
accessed 30 October, http://maliactu.net/mali-iyad-ag-ghaly-ancardine-souhaiterait-un-
cessez-le-feu
![Page 29: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/29.jpg)
28
2 Christia, Fotini, Alliance formation in civil wars. (Cambridge University Press, 2012). 3 Lacher, Wolfram. Organized crime and conflict in the Sahel-Sahara region. Vol. 1.
(Washington DC: Carnegie Endowment for International Peace, 2012); Bøås, Morten.
2015. The Politics of Conflict Economies: Miners, Merchants and Warriors in the
African Borderland. (London, Routledge). 4 Lecocq, Baz, Gregory Mann, Bruce Whitehouse, Dida Badi, Lotte Pelckmans, Nadia Belalimat,
Bruce Hall, and Wolfram Lacher, “One Hippopotamus and Eight Blind Analysts: A
Multivocal Analysis of the 2012 Political Crisis in the Divided Republic of Mali,”
Review of African Political Economy 40, no. 137 (2013): 343-357; Harmon, Stephen A.
Terror and insurgency in the Sahara-Sahel region: corruption, contraband, jihad and
the Mali war of 2012-2013 (Ashgate Publishing, Ltd., 2014); Wehrey, Frederic and
Boukhars, Anouar (eds) Perilous Desert. Insecurity in the Sahara (Carnegie
Endowment, 2013); Dowd, Caitriona “Fragmentation, conflict, and competition:
Islamist anti-civilian violence in sub-Saharan Africa”, Terrorism and Political Violence
(2016) DOI:10.1080/09546553.2016.1233870; Gow, James, Olonisakin, Funmi,
Dijxhoorn, Ernst (eds) Militancy and Violence in West Africa. (London, Routledge,
2013). 5 Walther, Olivier and Antonin Tisseron “Strange Bedfellows: A Network Analysis of Mali’s
Northern Conflict,” The Broker, (2015) Dec 18; Bencherif, Adib and Aurélie Campana.
“Alliances Of Convenience: Assessing The Dynamics Of The Malian Insurgency,”
Mediterranean Politics (2016): 1-20.http://dx.doi.org/10.1080/13629395.2016.1230942 6 Bakke, Kristin M., Kathleen Gallagher Cunningham, and Lee J.M. Seymour, “A Plague of
Initials: Fragmentation, Cohesion, And Infighting in Civil Wars,” Perspectives on
Politics 10, no. 02 (2012): 265-283; Cunningham, Kathleen Gallagher, Kristin M.
Bakke, and Lee JM Seymour. “Shirts Today, Skins Tomorrow Dual Contests And The
Effects Of Fragmentation In Self-Determination Disputes,” Journal of Conflict
Resolution 56, no. 1 (2012): 67-93. 7 Findley, Michael and Peter Rudloff, "Combatant Fragmentation and the Dynamics of Civil
Wars,” British Journal of Political Science 42, no. 04 (2012): 879-901. 8 Walther, Olivier J. and Dimitris Christopoulos, “Islamic Terrorism and The Malian Rebellion,”
Terrorism and Political Violence 27, no. 3 (2015): 497-519; Zheng, Quan, David B.
![Page 30: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/30.jpg)
29
Skillicorn, and Olivier Walther, “Signed Directed Social Network Analysis Applied To
Group Conflict,” In 2015 IEEE International Conference on Data Mining Workshop
(ICDMW), pp. 1007-1014, DOI 10.1109/ICDMW.2015.107. 9 Hegre, Håvard, Gudrun Østby, and Clionadh Raleigh, "Poverty And Civil War Events A
Disaggregated Study Of Liberia," Journal of Conflict Resolution 53, no. 4 (2009): 598-
623; Cederman, Lars-Erik and Kristian Skrede Gleditsch, "Introduction To Special
Issue On" Disaggregating Civil War," Journal of Conflict Resolution (2009); Salehyan,
Idean. Rebels without borders. (Cornell University Press, 2009); Zammit-Mangion,
Andrew, Michael Dewar, Visakan Kadirkamanathan, Anaïd Flesken, and Guido
Sanguinetti. Modeling Conflict Dynamics With Spatio-Temporal Data (Springer
International Publishing, 2013); Dowd, Caitriona, "Cultural And Religious Demography
And Violent Islamist Groups In Africa," Political Geography 45 (2015a): 11-21;
Metternich, Nils W., Shahryar Minhas, and Michael D. Ward, "Firewall? or Wall on
Fire? A Unified Framework of Conflict Contagion and the Role of Ethnic Exclusion,"
Journal of Conflict Resolution (2015): 0022002715603452. 10 For a review see O’Loughlin, John and Clionadh Raleigh, “The Spatial Analysis Of Civil War
Violence,” in A Handbook of Political Geography, edited by Kevin Cox, Murray Low,
and Jennifer Robinson (Thousand Oaks, Sage, 2008), 493–508. 11 Cederman, Lars-Erik, Halvard Buhaug, and Jan Ketil Rød, "Ethno-Nationalist Dyads and Civil
War A GIS-Based Analysis," Journal of Conflict Resolution 53, no. 4 (2009): 496-525;
Metternich, Nils W., Shahryar Minhas, and Michael D. Ward, "Firewall? or Wall on
Fire? A Unified Framework of Conflict Contagion and the Role of Ethnic Exclusion,”
Journal of Conflict Resolution (2015): 0022002715603452; Phillips, Brian J. "Enemies
With Benefits? Violent Rivalry And Terrorist Group Longevity,” Journal of Peace
Research 52, no. 1 (2015): 62-75. 12 Kaldor, Mary. New And Old Wars: Organised Violence In A Global Era. (Stanford, Stanford
University Press, 2012). 13 Williams, Paul. War and Conflict in Africa. (Cambridge: Polity Press, 2016). 14 Weiss, Caleb. “Merger of al Qaeda groups threatens security in West Africa,” The Long War
Journal, 18 March, 2017. 15 Pearlman, Wendy and Kathleen Gallagher Cunningham, "Nonstate Actors, Fragmentation, And
Conflict Processes,” Journal of Conflict Resolution 56, no. 1 (2012): 3-15; Dowd,
![Page 31: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/31.jpg)
30
Caitriona, "Actor Proliferation And The Fragmentation Of Violent Groups In Conflict,”
Research & Politics 2, no. 4 (2015b): 2053168015607891; Asal, Victor H., Hyun Hee
Park, R. Karl Rethemeyer, and Gary Ackerman, "With Friends Like These… Why
Terrorist Organizations Ally,” International Public Management Journal 19, no. 1
(2016): 1-30. 16 Staniland, Paul. Networks of rebellion: Explaining insurgent cohesion and collapse (Cornell
University Press, 2014). 17 Shapiro, Jacob N. The Terrorist's Dilemma: Managing violent covert organizations (Princeton
University Press, 2013). 18 Cunningham, Kathleen Gallagher, "Actor Fragmentation And Civil War Bargaining: How
Internal Divisions Generate Civil Conflict,” American Journal of Political Science 57,
no. 3 (2013): 659-672. 19 Cunningham, Kathleen Gallagher, "Divide And Conquer Or Divide And Concede: How Do
States Respond To Internally Divided Separatists?," American Political Science Review
105, no. 02 (2011): 275-297. 20 Bakke, Kristin M., Kathleen Gallagher Cunningham, and Lee J.M. Seymour. "A Plague Of
Initials: Fragmentation, Cohesion, And Infighting In Civil Wars,” Perspectives on
Politics 10, no. 02 (2012): 265-283; Cunningham, Kathleen Gallagher, Kristin M.
Bakke, and Lee JM Seymour. "Shirts Today, Skins Tomorrow Dual Contests And The
Effects Of Fragmentation In Self-Determination Disputes,” Journal of Conflict
Resolution 56, no. 1 (2012): 67-93. 21 Cunningham, David E. "Veto Players And Civil War Duration,” American Journal of Political
Science 50, no. 4 (2006): 875-892. 22 Findley, Michael and Peter Rudloff. "Combatant Fragmentation And The Dynamics Of Civil
Wars,” British Journal of Political Science 42, no. 04 (2012): 879-901. 23 Fjelde, Hanne and Desirée Nilsson. "Rebels Against Rebels Explaining Violence Between Rebel
Groups,” Journal of Conflict Resolution 56, no. 4 (2012): 604-628. 24 Nygård, Håvard Mokleiv and Michael Weintraub. "Bargaining Between Rebel Groups And
The Outside Option Of Violence,” Terrorism and Political Violence 27, no. 3 (2015):
557-580.
![Page 32: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/32.jpg)
31
25 Bapat, Navin A. and Kanisha D. Bond. "Alliances Between Militant Groups,” British Journal
of Political Science 42, no. 4 (2012): 793-824. 26 Fjelde, Hanne and Desirée Nilsson. "Rebels against rebels explaining violence between rebel
groups,” Journal of Conflict Resolution 56, no. 4 (2012): 604-628. 27 Akcinaroglu, Seden. "Rebel interdependencies and civil war outcomes,” Journal of Conflict
Resolution 56, no. 5 (2012): 879-903. 28 Christia, Fotini. Alliance formation in civil wars. (Cambridge University Press, 2012). 29 Pearlman, Wendy and Kathleen Gallagher Cunningham. "Nonstate Actors, Fragmentation,
And Conflict Processes,” Journal of Conflict Resolution 56, no. 1 (2012): 3-15; Ingiriis,
Mohamed Haji. "African Conflicts and Informal Power: Big Men and Networks ed. by
Mats Utas (review),” Africa Today 60, no. 4 (2014): 92-93. 30Brass, Daniel J. and David M. Krackhardt. Power, politics, and social networks in
organizations, in Politics in Organizations: Theory and Research Consideration, edited
by Gerald R. Ferris, Treadway Darren C. (New York: Routledge, 2012), 355-375; Burt,
Ronald S. Structural holes: The social structure of competition. (Harvard University
Press, 2009). 31 Newman, Mark. Networks: an introduction. (Oxford University Press, 2010). 32 Maoz, Zeev, Ranan D. Kuperman, Lesley Terris, and Ilan Talmud. "Structural Equivalence
And International Conflict A Social Networks Analysis,” Journal of Conflict Resolution
50, no. 5 (2006): 664-689. 33 Flint, Colin, Paul Diehl, Jürgen Scheffran, John Vasquez, and Sang-hyun Chi.
"Conceptualizing Conflictspace: Toward A Geography Of Relational Power And
Embeddedness In The Analysis Of Interstate Conflict,” Annals of the Association of
American Geographers 99, no. 5 (2009): 827-835; Radil, Steven M. and Colin Flint.
"Exiles and arms: the territorial practices of state making and war diffusion in post–
Cold War Africa,” Territory, Politics, Governance 1, no. 2 (2013): 183-202. 34 Everett, Martin G. and Stephen P. Borgatti. "Networks Containing Negative Ties,” Social
Networks 38 (2014): 111-120. 35 Huitsing, Gijs, Marijtje AJ Van Duijn, Tom AB Snijders, Peng Wang, Miia Sainio, Christina
Salmivalli, and René Veenstra. "Univariate And Multivariate Models Of Positive And
![Page 33: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/33.jpg)
32
Negative Networks: Liking, Disliking, And Bully–Victim Relationships,” Social
Networks 34, no. 4 (2012): 645-657. 36 Everett, Martin G. and Stephen P. Borgatti. "Networks Containing Negative Ties,” Social
Networks 38 (2014): 111-120. 37 Labianca, Giuseppe and Daniel J. Brass. "Exploring The Social Ledger: Negative
Relationships And Negative Asymmetry In Social Networks In Organizations,”
Academy of Management Review 31, no. 3 (2006): 596-614; Grosser, Travis J., Virginie
Lopez-Kidwell, and Giuseppe Labianca. "A Social Network Analysis Of Positive And
Negative Gossip In Organizational Life,” Group & Organization Management 35, no. 2
(2010): 177-212; Rambaran, J. Ashwin, Jan Kornelis Dijkstra, Anke Munniksma, and
Antonius HN Cillessen. "The development of adolescents’ friendships and antipathies:
A longitudinal multivariate network test of balance theory,” Social Networks 43 (2015):
162-176. 38 Doreian, Patrick and Andrej Mrvar. "Structural Balance And Signed International Relations,”
Journal of Social Structure 16 (2015): 1. 39 Smith, Jason M., Daniel S. Halgin, Virginie Kidwell-Lopez, Giuseppe Labianca, Daniel J.
Brass, and Stephen P. Borgatti. "Power In Politically Charged Networks,” Social
Networks 36 (2014): 162-176. 40 Raleigh, Clionadh and Caitriona Dowd. "Armed Conflict Location And Event Data Project
(ACLED) Codebook,” (2015): 24.
content/uploads/2015/01/ACLED_Codebook_2015.pdf 41 Algeria, Benin, Burkina Faso, Cameroon, Chad, Gambia, Ghana, Guinea, Guinea Bissau,
Ivory Coast, Liberia, Mauritania, Morocco, Libya, Niger, Nigeria, Mali, Sierra Leone,
Senegal, Tunisia and Togo. 42 Freeman, Linton C. "Centrality In Social Networks Conceptual Clarification,” Social networks
1, no. 3 (1979): 215-239. 43 Zheng, Quan and David B. Skillicorn. "Spectral Embedding Of Signed Networks,” In SIAM
International Conference on Data Mining, pp. 55-63. 2015., DOI
10.1137/1.9781611974010.7; Zheng, Quan, David B. Skillicorn, and Olivier Walther.
"Signed Directed Social Network Analysis Applied To Group Conflict,” In 2015 IEEE
![Page 34: Political fragmentation and alliances among armed non](https://reader036.vdocuments.us/reader036/viewer/2022072910/62e2bc47a78c7925e4160c55/html5/thumbnails/34.jpg)
33
International Conference on Data Mining Workshop (ICDMW), pp. 1007-1014. IEEE,
2015. , DOI 10.1109/ICDMW.2015.107. 44 Huitsing, Gijs, Marijtje AJ Van Duijn, Tom AB Snijders, Peng Wang, Miia Sainio, Christina
Salmivalli, and René Veenstra. "Univariate And Multivariate Models Of Positive And
Negative Networks: Liking, Disliking, And Bully–Victim Relationships,” Social
Networks 34, no. 4 (2012): 645-657. 45 Radil, Steven. “Toward a network theory of the diffusion of ‘new wars’”, Paper presented at
the DASTI workshop on transnational extremist organizations, Rutgers University, 19-
20 September 2016. 46 Williams, Paul. War and Conflict in Africa. (Cambridge: Polity Press, 2016). 47 Labianca, Giuseppe and Daniel J. Brass. "Exploring The Social Ledger: Negative
Relationships And Negative Asymmetry In Social Networks In Organizations,”
Academy of Management Review 31, no. 3 (2006): 596-614. 48 Smith, Jason M., Daniel S. Halgin, Virginie Kidwell-Lopez, Giuseppe Labianca, Daniel J.
Brass, and Stephen P. Borgatti. "Power In Politically Charged Networks,” Social
Networks 36 (2014): 162-176. 49 Dakono, Baba. 2013. “Who’s Who In Northern Mali?” ISS, Institute for Security Studies,
https://www.issafrica.org/iss-today/whos-who-in-northern-mali 50 Details of the mathematical constructions can be found in Zheng, Quan and Skillicorn, David B. Social Networks with Rich Edge Semantics. (Taylor and Francis, 2017).