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Terrorism and Political Violence

ISSN: 0954-6553 (Print) 1556-1836 (Online) Journal homepage: http://www.tandfonline.com/loi/ftpv20

Political Fragmentation and Alliances amongArmed Non-state Actors in North and WesternAfrica (1997–2014)

Olivier Walther, Christian Leuprecht & David B. Skillicorn

To cite this article: Olivier Walther, Christian Leuprecht & David B. Skillicorn (2017): PoliticalFragmentation and Alliances among Armed Non-state Actors in North and Western Africa(1997–2014), Terrorism and Political Violence, DOI: 10.1080/09546553.2017.1364635

To link to this article: http://dx.doi.org/10.1080/09546553.2017.1364635

Published online: 26 Sep 2017.

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Political Fragmentation and Alliances among Armed Non-stateActors 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 ofBusiness, Government & Law, Flinders University of South Australia

ABSTRACTDrawing on a collection of open source data, the article uses networkanalysis to represent alliances and conflicts among 179 organizationsinvolved in violence in North and Western Africa between 1997 and2014. Owing to the fundamentally relational nature of internecine vio-lence, this article investigates the way the structural positions of conflict-ing parties affect their ability to resort to political violence. To this end, wecombine two spectral embedding techniques that have previously beenconsidered separately: one for directed graphs that takes into account thedirection of relationships between belligerents, and one for signed graphsthat takes into consideration whether relationships between groups arepositive or negative. We hypothesize that groups with similar allies andfoes have similar patterns of aggression. In a region where alliances arefluid and actors often change sides, the propensity to use political vio-lence corresponds to a group’s position in the social network.

KEYWORDSAlliance; fragmentation;North-West Africa; politicalviolence; social networkanalysis

Introduction

In a recent letter addressed to the President of the Islamic Council of Mali on September27, 2016, Iyad ag Ghaly, the leader of the jihadist group Ansar Dine, announced that hewould unilaterally cease attacks throughout Mali “and especially in the North of thecountry.” Signed on behalf of “Ansar Dine and its allies,” the letter further explainedthat the group would not renounce its goal of imposing Islamic law (sharia) but wouldwork towards a ceasefire to “ensure the security of persons and their property andpromote 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 thecountry. The subject of much debate, this is the latest development in a tortuous military careerfor 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 terroristgroup, and fugitive. The fact that a militant such as he has successively worked for and againstthe state, withinMali and abroad, and as a civilian and amilitary leader, illustrates just how fluidmany modern African conflicts are: commanders and rank-and-file fighters frequently shiftallegiances among regular forces and armed non-state actors. A similar volatility characterizes

CONTACT Christian Leuprecht christian.leuprecht@rmc.ca Royal Military College of Canada, Political Science andEconomics, P.O. Box 17,000, Station Forces, Kingston, Ontario, K7K 7B4 Canada.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ftpv.

TERRORISM AND POLITICAL VIOLENCEhttps://doi.org/10.1080/09546553.2017.1364635

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political allegiances between governments andmyriad often ephemeral armed groups, who splitand coalesce as new opportunities arise. While groups that appear at odds one day may be alliesthe next, splinter groups formed after leaders fall out with one another might nonethelesscollaborate against a third party.

The complexmotivations and outcomes of such alliances and conflicts have received growingattention over the last decade.2 On the one hand, a number of detailed qualitative studies havecontributed to documenting how relationships among rebels, religious extremists, and traffick-ers that developed in North andWestern Africa were mainly based on corruption around illegalflows of drugs, weapons, and migrants3 and had fundamentally changed the political landscapeof the region.4 Mali, with its many short-lived alliances between secessionist and Islamist groupswith conflicting agendas, has been of particular interest.5 On the other hand, a growing body ofquantitative studies identifies internal fragmentation, conflicts, and alliances between armedgroups as a crucial explanation for the onset and diffusion of internecine violence6 and the oftenelusive quest for peace settlements.7

This article bridges these strands of literature through a more formal approach to socialnetworks of belligerents in the region. Examining the relationships between alliances andconflicts as a putative explanation for the patterns of violence in North and WesternAfrica, the article posits a relational approach to the study of the structure of relationshipsamong state and non-state actors. In doing so, it builds on a growing body of literaturethat takes advantage of the recent availability of disaggregated data to map and model tiesbetween and within violent organizations.8

The article proceeds as follows. The second section reviews the literature on conflict andsigned networks and shows that greater access to geo-referenced data and the use of spatialstatistical 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 ofbelligerents. The fourth section models the structural position of actors in conflict. The lastsection 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 ofsatellite and disaggregated data has spawned innovative approaches to investigating the onsetand diffusion of political violence across time and space.9 The concomitant proliferation ofpolitical and economic predictors, on which the spatial-analytical approach can draw, nowincludes factors as diverse as the nature of government, ethnic divisions, poverty, income,inequality, number and morale of troops, frequency of droughts, and endowment of naturalresources.10

Some factors that may explain why groups resort to violence are also related to thestructure of relationships that connect actors in conflict.11 Modern African conflicts bringtogether a multitude of state and non-state belligerents that include regular military forces,pro-government, ethnic, and religious militias, rebels, secessionist and self-determinationmovements, violent Islamist groups, warlords, thugs, and criminals.12 The relationshipswithin and between these actors are often characterized by a bewildering array of alliancesand conflicts.13 In North and Western Africa, for example, the Salafist Group forPreaching and Combat (GSPC)—a splinter group of the Algerian Armed Islamic Group

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—rebranded itself as Al Qaeda in the Islamic Maghreb (AQIM) in 2007. Some of itsmembers broke off in 2011 to form Movement for Unity and Jihad in West Africa(MUJAO) while others formed Al Moulathamoun and Al Mouakaoune Biddam. In2013, MUJAO merged with Al Moulathamoun to form Al Mourabitoune, which, in2015, was renamed Al Qaeda in West Africa. More recently, AQIM, Ansar Dine, AlMourabitoune, and the Macina Liberation Front merged to form the “Group for theSupport of Islam and Muslims” (Jama’at Nusrat al-Islam wal-Muslimin).14 These mergers,splits, and name changes suggest that organizations affiliated with Al Qaeda share acommon historical and ideological background and form several components of a single,opportunistic network, rather than independent entities. Causes and consequences of thepatterns of violence associated with such alliances and conflicts have received increasingattention over recent years.15

Research focusing on intragroup dynamics suggests that the internal structure of warringfactions is central to explaining patterns of violence of non-state actors, be they insurgents16

or terrorists.17 Social ties forged before and during war between belligerents make violentorganizations more cohesive, less prone to factionalization, and facilitate recruitment andallegiance during conflicts. Internal divisions in self-determination movements are asso-ciated with a greater probability of civil wars because the multiplication of belligerentscreates political uncertainties as to what concessions could be made and what commitmentscould resolve a conflict through non-violent means.18 Internally divided self-determinationmovements are also more likely to receive concessions than unitary ones because statesoften “divide and concede” rather than “divide and conquer.”19 While fragmented groupsseem to increase the intensity of violence, particularly against civilians,20 the effect of thefragmentation of violent groups on the duration of conflicts remains controversial. Somestudies suggest that fragmentation complicates peace settlements by multiplying the num-ber of “veto players” that must approve a settlement.21 Others argue that fragmentationaccelerates them by weakening belligerents and forcing them to cooperate.22

Studies focusing on intergroup dynamics suggest that violence between non-state actors canbe understood as a means for access to resources and political leverage to fight centralgovernments.23 This explains why rebel groups often fight each other instead of formingcoalitions,24 particularly when the government lacks repressive power.25 Research on armedconflict between non-state actors shows that inter-rebel violence is more likely in drug produc-tion areas, where rebel groups have established control over territory beyond the government’sreach and are numerically strong, and where states are unable to exercise their authority.26 Thatintergroup alliances also shape the outcome of civil wars is less documented. While intergroupalliances rarely lead to victory, interdependencies between rebel groups bring valuable resourcessuch as intelligence and tactical support that can be used against a well-organized and capablegovernment to avoid defeat.27 In conflict situations where an external party, such as a foreignmilitary power, can enforce cooperation between warring parties that leads to a peace settle-ment, armed groupsmight have an interest in forming coalitions and aligning with the side theybelieve to have the greatest chance of emerging victorious.28

Recent studies on fragmentation and alliances among state and non-state actorsapproach violence as a relational process whose structure enables and constrains action.29

Other network analysis has already observed that social actors who wish to reduce theirstructural constraints can develop network tactics to alter the structure—rather than thebehavior of others—to their advantage.30 The ready availability of disaggregated data,

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combined with recent conceptual and computational advances in network analysis, hasallowed a growing number of studies to test such assumptions empirically using socialnetwork analysis (SNA). SNA is the study of individual actors, groups, organizations, orcountries, represented by the nodes of the network, and the relationships between theseactors, represented by their links. As both a paradigm of social interactions based on graphtheory and a method, SNA seeks to understand networks by mapping out the ties betweennodes as they are rather than how they ought to be or are expected to be.31

SNA is particularly adept at capturing the complexity of conflict situations due to itsability to describe, represent, and model signed networks, i.e., networks that contain bothpositive and negative relations. Positive ties develop to overcome collective-action pro-blems, enforce trust and ideology, coordinate activities at a distance, distribute resources,or disseminate ideas and decisions. Alliances between states are typical of positive-tienetworks. By contrast, negative ties develop among actors that dislike, avoid, or fight oneanother. For positive and negative ties, SNA can be used to study the structure andfunction of the network as a whole, and the role of each node in the group in relationto others. Network approaches have been used to verify whether states with commonenemies have fought one another,32 how alliances or rivalries between states could explainthe diffusion of World War I on a global scale, and to illustrate the increasing number ofalliances between African states since the end of the Cold War.33

Networks with positive ties are known to be structured differently from those withnegative ties.34 Networks based on friendship, alliance, and collaboration are denser andmore clustered around actors that share similar values than networks with negative ties,because individuals and organizations tend to have more friends than enemies.35 Positive-tie networks also harness more resources, ideas, and knowledge than negative-tie networkssince the latter are driven by hatred, avoidance, or conflict. As a result, many centralitymeasures based on the assumption that social networks serve as conduits for flows ofinformation, advice, or influence, such as betweenness or closeness centrality, are unrea-listic in the case of actors in conflict.36 Networks with negative ties are also well known fortheir low level of transitivity, a principle that assumes that two actors that share aconnection to a third actor are likely to be connected themselves.

A growing literature suggests that, despite their differences, positive- and negative-tienetworks should be analyzed simultaneously.37 One way to incorporate both allies andadversaries is to use structural balance theory, which argues that social relations are stableif they contain an even number of negative ties. Stable groups of three actors (known astriads) are theoretically stable if everyone likes everyone else, or if two actors are inconflict with a third party.38 Over time, unstable triads theoretically evolve towards stabletriads, because instability creates tensions that can only be resolved by altering views,behaviors, and alliances. Another approach to signed networks is to model the structuralautonomy and constraints of actors. Smith et al. argue that an actor’s political indepen-dence is constrained both by its potential to reach other actors’ resources and by thestructural position of allies and enemies.39 Being connected to a single ally that is notunder threat considerably reduces the autonomy of actors in signed networks, while adiversified network of allies enhances autonomy.

This article adopts a complementary approach. Instead of assuming that politicalviolence is explained by attributes of the belligerents or by exogenous factors, we proposethat the propensity to use political violence corresponds to a group’s position in the social

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network. To this end, the initial part of our analysis aims at representing how actors inconflict are connected to their allies and enemies. We use centrality measures to identifysubclusters of actors where conflict or cooperation is particularly developed, and highlightthe main structural differences between positive- and negative-tie networks. Since enemiesand allies are inextricably linked in real-life networks, the subsequent analytical part of thearticle considers positive and negative ties simultaneously. Spectral embedding techniquesmake it possible to place the nodes that represent organizations at the position that bestbalances the “pull” of allies against the “push” of enemies. This makes it possible to modelthe balance between the relative effects of having allies and foes simultaneously. We alsotake into account the fundamentally asymmetric nature of conflicts and consider whethergroups attack more or less than they are attacked. Combining signed and directed net-works, we expect groups with similar allies and foes and similar patterns of aggression toform 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. ACLEDprovides a comprehensive list of political events by country between 1997 and 2014.40 Thefifth version of the data was used to select 37 armed non-state actors (Appendix 1) in 21North and Western Africa countries,41 their allies and their enemies, excluding non-identified Islamist and Libyan militias (see Appendix 1). The scope was limited to eventswith the following seven referents: Battle—no change of territory; Battle—Non-state actorovertakes territory; Battle—Government regains territory; Riots and protests; Violenceagainst civilians; and Remote violence. This generated a list of 3231 events comprised of179 organizations and 27,791 fatalities.

The ACLED dataset describes (up to) four groups in each incident: an attacker (A), acollaborator in the attack (B), a target (C), and a potentially assisting group that may also bea secondary target (D). This data is used to build a social network in which the nodes aregroups, with positively weighted directed ties between allies (B to A, and D to C) andnegatively 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 AnsarDine (C) and MUJAO (D) on the other hand, claimed 11 lives, including Islamist leaderAbdel Krim, and left 60 injured (ACLED incident 486MLI). Incidents are aggregated so thatthe ties between any pair of groups reflect all of their interactions. Ties can be both positiveand negative, and in both directions, between the same two groups. Direction is a proxy forintentionality: a group on the offensive makes a conscious decision to attack while thedefender has no choice, and other groups must decide whether to join. These decisionsreflect a calculus of advantage or ideological alignment.

The resulting graph is analyzed in two steps. First, we map the networks containingnegative and positive ties separately and analyze the most prominent actors using severalcentrality measures. Because negative-tie networks do not serve as conduits for flows ofinformation, advice, or influence, we use degree centrality, which simply refers to thestandardized number of ties each node has, and eigenvector centrality, which refers to thenumber of nodes adjacent to a given node, weighted by centrality, and indicate whethernodes are connected to other well-connected nodes. For our positive-tie network, we use

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eigenvector centrality and betweenness centrality, which measures the number of shortestpaths from all nodes to all others that pass through that node.42

Second, we combine both positive and negative ties into a single network, and embedthis network in a geometric space in such a way that the distance between each pair ofpoints accurately reflects the balance between the “pull” from collaborating groups and the“push” from aggression between them. These distances are globally integrated as afunction of immediate neighbors (i.e., actors who cooperate to fight each other) as wellas neighbors of neighbors and, in fact, the structure of the entire graph. This integrationmakes the process challenging: positive relationships are naturally transitive (“the ally ofmy ally could plausibly become my ally”) but negative relationships are not “the enemy ofmy enemy is my friend”. The adjacency matrices that describe positive and negative tiescombine both kinds of ties. The representation is then normalized so that well-connectednodes are central and poorly connected nodes peripheral.43 This Laplacian matrix is usedto embed the graph in a geometry where position is meaningful (well-connected nodes areplaced centrally), and proximity represents similarity (similar nodes cluster together). Setsof “bad” armed non-state actors and (supposedly) “good” governmental forces and civilsociety tend to form polar opposites in some dimensions of the representation. Sinceproximity 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 tothose actors with which it is in conflict. The size of the nodes in Figure 1 is proportional tothe number of ties (or degree).

Three main clusters emerge: the Nigerian cluster that is polarized by Boko Haram; theTrans-Saharan cluster that is composed of groups affiliated with Al Qaeda such as GSPCand AQIM and their enemies; and the Libyan cluster that is composed of myriad Islamistbrigades and pro-government forces. With a density of only 0.023, the network is verysparse, which is typical of networks that are made up exclusively of negative ties: thenumber of enemies a group can have is often more limited than the number of potentialallies.44 The network also has a low level of transitivity: in only 1.2% of the triads enemiesof enemies are in fact enemies, while in most cases (98.8%), enemies of enemies arefriends. 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 wereactually in conflict, which fuels the political and spatial diffusion of the Syrian conflict.45

Finally, organizations with adverse attributes tend to be in conflict with one other, atendency known as heterophily. This can be tested using the E/I index, which calculatesthe difference between external and internal ties for each group of actors (government,rebels, militias, civilians, Islamists, and external forces), divided by the total number ofties. The E/I index for the network is positive (0.899) and statistically significant (chancesof getting the result right by guessing are less than 1%), which confirms that actors clashwith organizations that are not in the same category.

In terms of adversaries and victims (Table 1), the bloodiest conflicts have seen civiliansin conflict with state and non-state actors. Boko Haram is by far the bloodiest armed

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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), Fulanimilitias (2446), and the military (2382) also claimed many civilian victims. Clashesinvolving the Algerian GIA and civilians were particularly deadly in the late 1990s, with6212 victims reported in the database. The campaigns of civilian massacres adopted byGIA explain why some of its former members, such as Hassan Hattab, defected to form

Figure 1. Negative ties between organizations involved in violent events, 1997–2014.Note: Isolates are not shown.

Table 1. Bloodiest conflicts between actors, 1997–2014Actor 1 Actor 2 Fatalities

Civilians (Nigeria) Boko Haram 6409Civilians (Algeria) GIA Armed Islamic Group 6212Boko Haram Military forces of Nigeria 5447Civilians (Nigeria) Unidentified armed group (Nigeria) 3556Civilians (Nigeria) Fulani Ethnic Militia (Nigeria) 2446Civilians (Nigeria) Military forces of Nigeria 2382NLA National Liberation Army (Libya) Military forces of Libya 1740Christian Militias (Nigeria) Muslim militia (Nigeria) 1739Civilians (Libya) NATO forces 1367AQIM Military forces of Algeria 1074Military 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.

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GSPC in 1998. Finally, the National Liberation Army and the Libyan Armed Forcesclashed during the Libyan civil war in 2011 (1740 victims). More than 1350 victims arealso reported as a consequence of NATO military intervention, mostly civilians. Generallyspeaking, these figures confirm earlier studies: most victims of African conflicts werecivilians who either died at the hands of state or non-state armed groups, or from theeffects of displacement, malnutrition, and disease.46

As expected, the network is composed of few highly central organizations (Table 2) sincebeing in conflict with many adversaries simultaneously is widely regarded as a liability ratherthan an asset.47 Among armed non-state actors, AQIM scores highest on degree and eigenvectorcentrality, which indicates that it has the greatest number of enemies and is connected to otheractors that also have many enemies, such as the military and police forces of Algeria. This is aninteresting result: if it is a pure liability to have many enemies, then having enemies that arethemselves involved in many conflicts offers more autonomy to AQIM. The ideal structuralsituation for an actor embedded in a signed network is to have enemies that are constrained bynumerous threats that affect the outcomes of military operations, reduce their ability tocoordinate activities across the region, and limit their ability to cooperate to achieve theirpolitical or religious goals.48 MUJAO, GSPC, and GIA also occupy a prominent structuralposition due to their conflicts with civilians and armed forces in several countries. Otherprominent actors include Boko Haram, which stands out for being connected to many otheractors who themselves have few connections to one another, and Libyan groups such as Ansaral-Sharia and Libya Shield Brigade.

The structure of the network of enemies contrasts starkly with the one showing howorganizations involved in violent events have collaborated across the region. As depicted inFigure 2, the positive-tie network is divided into three main unconnected groups of allies, onetriad that connects an unidentified armed group to BokoHaram andAnsaru, and three dyads.

The main cluster on the left is structured around North and West African military andpolice 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. MNLAresulted from the fusion of a peaceful organization that was defending the rights of thelocal Tuareg population, an armed group involved in several rebellions, an organizationinspired by the Salafist ideology, and Tuareg mercenaries who had formerly beenemployed in Libya.49 MNLA was initially allied with Ansar Dine before switching sidesand fighting alongside the French-led military forces in 2013. The two other clusters arestructured around the armed forces of Libya and their pro-government brigades andbattalions, the other around Islamist groups and ethnic and communal militias in Libya.

Table 2. Top-scoring nodes for selected centrality measures—negative tiesRank 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 0.071Std. Dev. 0.035 0.095

Source: ACLED. Note: Scores are indicated in brackets.

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Each cluster has a chain-like structure in which organizations are rather distant from oneanother. The Algerian Private Security Forces, for example, are eight steps away from AlMourabitoune.

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 tightlyconnected actors. This suggests that most governmental forces and armed non-state actorstend 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 thatface similar threats: there is no reported tie between the military forces of Libya and Algeria,or between the military forces of Cameroon and Nigeria.

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 Frenchmilitary forces play a role in bridging several African armed forces that would otherwisenot be connected, hence their high betweenness centrality. Once again, the isolation of

Figure 2. Positive ties between organizations involved in violent events, 1997–2014.Source: ACLED. Notes: green nodes refer to Islamist groups, red to government forces, yellow tocivilians, and blue to other actors.

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Boko Haram in Nigeria contrasts sharply with the network of alliances among otherSahelo-Saharan and Libyan groups.

Spectral Embedding

Spectral embedding computes a representation of a graph with edge weights (representingtie strength) by projecting it into a low-dimensional space in such a way that nodes thatare 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 tothe center. A spectral embedding may not be visually clear, in the sense that Figure 2 is,but it is guaranteed, mathematically, to be as accurate as possible in the given dimension-ality. The networks we derive from the ACLED data require extensions of the techniquesof spectral embedding because their edges are directed, and they have both positive andnegative weights.50 Once again the resulting embeddings are guaranteed to be the mostaccurate possible in the given dimensionality, given assumptions about the relativeimportance and positive versus negative ties. Spectral embeddings are intrinsically induc-tive techniques; they do not require analysts to hypothesize variables that might cause tiesto 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 net-work, 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 theties. Negative ties resulting from recorded attacks are shown in red and positive tiesresulting from alliances, or at least common purpose, are shown in green. The generalstructure is polar opposites that represent groups whose primary relationship is that theyattack 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, bothviolent and non-violent. The contrast is particularly evident for Boko Haram, and itsopposition to governmental forces and civilians from Nigeria and Cameroon, as well as forGIA-GSPC-AQIM, and its opposition to Algerian armed forces and civilians. The graphalso shows that the attack patterns of GIA, GSPC, and AQIM differ markedly from thoseof Ansar Dine, MUJAO, and Al Mourabitoune, which are located much closer to thecenter of Figure 3.

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 ofinteraction. We, therefore, compute measures of outward and inward aggression based not

Table 3. Top-scoring nodes for selected centrality measures—positive tiesRank 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 0.012St. Dev. 0.082 0.022

Source: ACLED. Calculations by the authors. Note: Scores are indicated in brackets.

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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 withwhich it interacts; therefore, the length of the embedded ties is more revealing than simplythe number of attacks. For example, the distance of a group from the center of theembedding reflects not only how many other groups attack it (or are attacked by it) butalso 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 theirstrategic intensity. Figure 4 plots groups at the same positions as in the spectral embed-ding presented in Figure 3 but labelled to distinguish their “levels of aggression”: thedifference between the outgoing aggression that each group causes and the incomingaggression to which it is subjected. The points are color-coded: red means a groupgenerates more aggression than it receives; orange means that the group generates someoutgoing aggression; and green means that there is no outgoing aggression (individualscores are presented in Appendixes 2, 3 and 4).

The vicinity of groups presented in Figure 4 allows us to distinguish armed non-stateactors (red, with almost all neighbors red as well) from national defense forces (red, butwith many orange or green neighbors). In other words, most of the polar opposites are

Figure 3. Spectral embedding showing positive and negative ties.

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structurally distinct. “Bad” net aggressors such as AQIM or Boko Haram tend to clustertogether, or are isolated; “good” aggressors such as militaries tend to cluster with orangeand 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 tobe located near their champions.

Northern Nigeria and Libya are particularly interesting as they involve many armednon-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 inNorthern Nigeria (Figure 5) clearly confirms that Boko Haram is in conflict with virtuallyeveryone, a situation comparable to that of Daesh in the Middle East, which opposes allgovernments and non-state actors—including Al Qaeda—in the region.

In Libya, spectral embedding conducted on 30 organizations highlights the ongoingconflict between weak alliances of pro-Islamist groups and weak alliances of pro-govern-ment forces (Figure 6). Islamist groups on the left of the graph are composed of Islamistmilitias such as Libya Dawn and Libya Shield, and of Jihadist groups close to Al Qaedasuch as the Revolutionaries Shura Council (BRSC), a coalition that includes Ansar al-

Figure 4. Spectral embedding showing levels of aggression.

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Sharia, the February 17 Brigade, and the Rafallah Sehati Brigade. These groups, based inTripoli and Benghazi, 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 ZintanMilitia, the Al-Sawaiq Battalion, and the Al Qaqa Brigade. Civilians and journalists arelocated near the internationally recognized authorities of Libya.

Figure 5. Spectral embedding showing positive and negative ties for 37 organizations in NorthernNigeria.Note: for the sake of clarity, Ansaru is not shown.

Figure 6. Spectral embedding showing positive and negative ties for 30 organizations in Libya.Note: for the sake of clarity, Ansaru is not shown.

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Conclusion

This article has illustrated the effectiveness of extending social network analysis to the structureof armed non-state actors in North andWest Africa, a region which, over the past 20 years, hasbecome more politically unstable. However, conventional social network measures fail in thesesettings. For example, measures such as betweenness are inappropriate because negativity doesnot “flow” in the way that positivity is conceived, and centrality is not a crucial property whennegativity separates nodes far from the center. Instead, ourmethodological contribution is basedon a novel approach that combines signed and directed graphs to highlight opposed groups anddistinguish among several kinds of aggressors as a function of their conflict patterns. In settingswhere groups form shifting alliances and oppositions, an approach that takes into account notonly local, pairwise relationships, but also global patterns that emerge, is needed for situationalawareness. Conventional social network cannot represent positive ties, negative ties and directedties simultaneously. Furthermore, these are not independent properties of a social network andso must be represented together.

In the process, the article advances theory on the fragmentation of conflict. We used opensource data to map how 179 organizations involved in political violence were structurallyconnected through conflict and alliances. Our analysis shows the extent to which the networkthat connects actors in conflict has a low density, a low level of transitivity, and contains fewcentral actors, three typical features of negative-tie networks. AQIM is unequivocally the mostconnected organization, both in terms of the overall number of actors with which the group is inconflict, and the respective centrality of its enemies. In network terms, this is a liability. Dividedinto several clusters, the positive-tie network has a long path-length distance, low density, andlow clustering coefficient, a structure that suggests that most organizations tend to build limitedalliances rather than broad coalitions across the region.

We then combined the two networks and modeled the effect of having friends and foessimultaneously. Using the attack relationships, we also measured the level of outgoing andincoming aggression of each group. From this approach,five categories emerge: a) neutral actors,represented in the middle of our graphs; b) victims, c) groups that are attacked more than theythemselves attack, and d) groups that counter violence and thus attack more than they areattacked (e.g., militaries)—and e) violent extremist groups that attack more than they areattacked, such as armed non-state actors. Groups that are net attackers are indistinguishable atthe level of individual behavior, but clearly separate into pro- and anti-violent extremism basedon the groups towhich they are close. This conclusion is in linewith our original proposition thatthe propensity to use political violence corresponds to a group’s position in the social networkrather than their actions per se. This raises situational awareness in a setting where it may bedifficult to distinguish “good” from “bad” actors based on their apparent goals or ideology.

These findings have policy implications for governments and external forces involved indeterring armed non-state actors. First, unlike their adversaries, armed non-state actors areconnected across the region. In recent years, several “Sahel” strategies have been initiated byorganizations as diverse as the European Union (2011), the United Nations (2013), theEconomic Community of West African States (2014), the African Union (2014), and theregional coordination framework G5 Sahel, to improve governance, security, and developmentin the region. Building institutional capacity around common interests is likely to pay off in aregion that is largely devoid of collective security institutions that could help countries coordi-nate, build trust, and go beyond ad hoc engagements. Precedent also suggests that states outside

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the regionwill continue to play a supporting rather than a leading role. In addition to supportingcapacity-building efforts already underway,Western governments should be prepared tomounta comprehensiveWhole-of-Government effort in support of local authorities that will minimizetheir local footprint, while optimizing outcomes. From a military perspective, the tenuouspersonal allegiances in the region call for a mobile and flexible military response. Regionalvolatility notwithstanding, operations Serval and Barkhane suggest that desert insurgents are notimpervious to external attack. As Western armies and their African allies become more mobileand flexible in their regional responses to political violence, desert insurgency proves to be adouble-edged sword that can also work against those who know the terrain best.

Acknowledgments

This article builds in part on Walther O, Leuprecht C, Skillicorn DB. 2018. Networks and spatialpatterns of extremist organizations in North and West Africa, in Walther O, Miles W. (eds). AfricanBorder Disorders. Abingdon, Routledge: 60-86. This article was made possible in part by a stay atthe Hanse-Wissenschaftskollege Institute for Advanced Study by Christian Leuprecht.

Notes1. MaliActu, “Mali: Iyad AgGhaly (Ancardine) Souhaiterait Un Cessez Le Feu” (2016), http://maliactu.

net/mali-iyad-ag-ghaly-ancardine-souhaiterait-un-cessez-le-feu (accessed October 30, 2016).2. Fotini Christia, Alliance Formation in Civil Wars (New York: Cambridge University Press,

2012).3. Wolfram Lacher, Organized Crime and Conflict in the Sahel-Sahara Region, Vol. 1

(Washington, DC: Carnegie Endowment for International Peace, 2012); Morten Bøås, ThePolitics of Conflict Economies: Miners, Merchants and Warriors in the African Borderland(London: Routledge, 2015).

4. Baz Lecocq, Gregory Mann, Bruce Whitehouse, Dida Badi, Lotte Pelckmans, Nadia Belalimat,Bruce Hall, and Wolfram Lacher, “One Hippopotamus and Eight Blind Analysts: A MultivocalAnalysis of the 2012 Political Crisis in the Divided Republic of Mali,” Review of AfricanPolitical Economy 40, no. 137 (2013): 343–57; Stephen A. Harmon, Terror and Insurgency inthe Sahara-Sahel Region: Corruption, Contraband, Jihad and the Mali War of 2012–2013(Farnham: Ashgate, 2014); Frederic Wehrey and Anouar Boukhars, eds., Perilous Desert:Insecurity in the Sahara (Washington, DC: Carnegie Endowment, 2013); Caitriona Dowd,“Fragmentation, Conflict, and Competition: Islamist Anti-civilian Violence in Sub-SaharanAfrica,” Terrorism and Political Violence (2016). doi:10.1080/09546553.2016.1233870; JamesGow, Funmi Olonisakin, and Ernst Dijxhoorn, eds., Militancy and Violence in West Africa(London: Routledge, 2013).

5. Olivier Walther and Antonin Tisseron, “Strange Bedfellows: A Network Analysis of Mali’sNorthern Conflict,” The Broker, December 18, 2015; Adib Bencherif and Aurélie Campana,“Alliances of Convenience: Assessing the Dynamics of the Malian Insurgency,” MediterraneanPolitics (2016): 1–20. doi:10.1080/13629395.2016.1230942.

6. Kristin M. Bakke, Kathleen Gallagher Cunningham, and Lee J.M. Seymour, “A Plague ofInitials: Fragmentation, Cohesion, and Infighting in Civil Wars,” Perspectives on Politics 10,no. 02 (2012): 265–83; Kathleen Gallagher Cunningham, Kristin M. Bakke, and Lee J.M.Seymour, “Shirts Today, Skins Tomorrow: Dual Contests and the Effects of Fragmentationin Self-Determination Disputes,” Journal of Conflict Resolution 56, no. 1 (2012): 67–93.

7. Michael Findley and Peter Rudloff, “Combatant Fragmentation and the Dynamics of CivilWars,” British Journal of Political Science 42, no. 04 (2012): 879–901.

8. Olivier J. Walther and Dimitris Christopoulos, “Islamic Terrorism and the Malian Rebellion,”Terrorism and Political Violence 27, no. 3 (2015): 497–519; Quan Zheng, David B. Skillicorn,

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and Olivier Walther, “Signed Directed Social Network Analysis Applied to Group Conflict,” in2015 IEEE International Conference on Data Mining Workshop (ICDMW), 1007–14,doi:10.1109/ICDMW.2015.107.

9. Håvard Hegre, Gudrun Østby, and Clionadh Raleigh, “Poverty and Civil War Events: ADisaggregated Study of Liberia,” Journal of Conflict Resolution 53, no. 4 (2009): 598–623;Lars-Erik Cederman and Kristian Skrede Gleditsch, “Introduction to Special Issue onDisaggregating Civil War,” Journal of Conflict Resolution (2009); Idean Salehyan, Rebels with-out Borders (Ithaca, NY: Cornell University Press, 2009); Andrew Zammit-Mangion, MichaelDewar, Visakan Kadirkamanathan, Anaïd Flesken, and Guido Sanguinetti, Modeling ConflictDynamics with Spatio-Temporal Data (Heidelberg, Germany: Springer International, 2013);Caitriona Dowd, “Cultural and Religious Demography and Violent Islamist Groups in Africa,”Political Geography 45 (2015): 11–21; Nils W. Metternich, Shahryar Minhas, and Michael D.Ward, “Firewall? or Wall on Fire? A Unified Framework of Conflict Contagion and the Role ofEthnic Exclusion,” Journal of Conflict Resolution (2015): 0022002715603452.

10. For a review see John O’Loughlin and Clionadh Raleigh, “The Spatial Analysis of Civil WarViolence,” in A Handbook of Political Geography, edited by Kevin Cox, Murray Low, andJennifer Robinson (Thousand Oaks, CA: Sage, 2008), 493–508.

11. Lars-Erik Cederman, Halvard Buhaug, and Jan Ketil Rød, “Ethno-Nationalist Dyads and CivilWar: A GIS-Based Analysis,” Journal of Conflict Resolution 53, no. 4 (2009): 496–525;Metternich, Minhas, and Ward, “Firewall? or Wall on Fire?” (see note 9 above); Brian J.Phillips, “Enemies with Benefits? Violent Rivalry and Terrorist Group Longevity,” Journal ofPeace Research 52, no. 1 (2015): 62–75.

12. Mary Kaldor, New and Old Wars: Organised Violence in a Global Era (Stanford, CA: StanfordUniversity Press, 2012).

13. Paul Williams, War and Conflict in Africa (Cambridge, MA: Polity Press, 2016).14. Caleb Weiss, “Merger of al Qaeda Groups Threatens Security in West Africa,” The Long War

Journal, March 18, 2017.15. Wendy Pearlman and Kathleen Gallagher Cunningham, “Nonstate Actors, Fragmentation, and

Conflict Processes,” Journal of Conflict Resolution 56, no. 1 (2012): 3–15; Caitriona Dowd, “ActorProliferation and the Fragmentation of Violent Groups in Conflict,” Research & Politics 2, no. 4(2015): 2053168015607891; Victor H. Asal, Hyun Hee Park, R. Karl Rethemeyer, and GaryAckerman, “With Friends Like These. . . Why Terrorist Organizations Ally,” International PublicManagement Journal 19, no. 1 (2016): 1–30.

16. Paul Staniland, Networks of Rebellion: Explaining Insurgent Cohesion and Collapse (Ithaca, NY:Cornell University Press, 2014).

17. Jacob N. Shapiro, The Terrorist’s Dilemma: Managing Violent Covert Organizations (Princeton,NJ: Princeton University Press, 2013).

18. Kathleen Gallagher Cunningham, “Actor Fragmentation and Civil War Bargaining: HowInternal Divisions Generate Civil Conflict,” American Journal of Political Science 57, no. 3(2013): 659–72.

19. Kathleen Gallagher Cunningham, “Divide and Conquer or Divide and Concede: How DoStates Respond to Internally Divided Separatists?,” American Political Science Review 105, no.02 (2011): 275–97.

20. Bakke, Cunningham, and Seymour, “A Plague of Initials” (see note 6 above); Cunningham,Bakke, and Seymour, “Shirts Today, Skins Tomorrow” (see note 6 above).

21. David E. Cunningham, “Veto Players and Civil War Duration,” American Journal of PoliticalScience 50, no. 4 (2006): 875–92.

22. Findley and Rudloff, “Combatant Fragmentation and the Dynamics of Civil Wars” (see note 7above).

23. Hanne Fjelde and Desirée Nilsson, “Rebels against Rebels: Explaining Violence between RebelGroups,” Journal of Conflict Resolution 56, no. 4 (2012): 604–28.

24. Håvard Mokleiv Nygård and Michael Weintraub, “Bargaining between Rebel Groups and theOutside Option of Violence,” Terrorism and Political Violence 27, no. 3 (2015): 557–80.

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25. Navin A. Bapat and Kanisha D. Bond, “Alliances between Militant Groups,” British Journal ofPolitical Science 42, no. 4 (2012): 793–824.

26. Fjelde and Nilsson, “Rebels against Rebels” (see note 23 above).27. Seden Akcinaroglu, “Rebel Interdependencies and Civil War Outcomes,” Journal of Conflict

Resolution 56, no. 5 (2012): 879–903.28. Christia, Alliance Formation in Civil Wars (see note 2 above).29. Pearlman and Cunningham, “Nonstate Actors, Fragmentation, and Conflict Processes” (see

note 15 above)30. Daniel J. Brass and DavidM. Krackhardt, “Power, Politics, and Social Networks in Organizations,”

in Politics in Organizations: Theory and Research Consideration, edited by Gerald R. Ferris andDarren C. Treadway (New York: Routledge, 2012), 355–75; Ronald S. Burt, Structural Holes: TheSocial Structure of Competition (Cambridge, MA: Harvard University Press, 2009).

31. Mark Newman, Networks: An Introduction (Oxford, UK: Oxford University Press, 2010).32. Zeev Maoz, 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): 664–89.

33. Colin Flint, Paul Diehl, Jürgen Scheffran, John Vasquez, and Sang-hyun Chi, “ConceptualizingConflictspace: Toward a Geography of Relational Power and Embeddedness in the Analysis ofInterstate Conflict,” Annals of the Association of American Geographers 99, no. 5 (2009):827–35; Steven M. Radil and Colin Flint, “Exiles and Arms: The Territorial Practices ofState Making and War Diffusion in Post–Cold War Africa,” Territory, Politics, Governance1, no. 2 (2013): 183–202.

34. Martin G. Everett and Stephen P. Borgatti, “Networks Containing Negative Ties,” SocialNetworks 38 (2014): 111–20.

35. Gijs Huitsing, Marijtje AJ Van Duijn, Tom AB Snijders, Peng Wang, Miia Sainio, ChristinaSalmivalli, and René Veenstra, “Univariate and Multivariate Models of Positive and NegativeNetworks: Liking, Disliking, and Bully–Victim Relationships,” Social Networks 34, no. 4(2012): 645–57.

36. Everett and Borgatti, “Networks Containing Negative Ties” (see note 34 above).37. Giuseppe Labianca and Daniel J. Brass, “Exploring the Social Ledger: Negative Relationships

and Negative Asymmetry in Social Networks in Organizations,” Academy of ManagementReview 31, no. 3 (2006): 596–614; Travis J. Grosser, Virginie Lopez-Kidwell, and GiuseppeLabianca, “A Social Network Analysis of Positive and Negative Gossip in Organizational Life,”Group & Organization Management 35, no. 2 (2010): 177–212; J. Ashwin Rambaran, JanKornelis Dijkstra, Anke Munniksma, and Antonius HN Cillessen, “The Development ofAdolescents’ Friendships and Antipathies: A Longitudinal Multivariate Network Test ofBalance Theory,” Social Networks 43 (2015): 162–76.

38. Patrick Doreian and Andrej Mrvar, “Structural Balance and Signed International Relations,”Journal of Social Structure 16 (2015): 1.

39. Jason M. Smith, Daniel S. Halgin, Virginie Kidwell-Lopez, Giuseppe Labianca, Daniel J. Brass, andStephen P. Borgatti, “Power in Politically Charged Networks,” Social Networks 36 (2014): 162–76.

40. Clionadh Raleigh 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, IvoryCoast, Liberia, Mauritania, Morocco, Libya, Niger, Nigeria, Mali, Sierra Leone, Senegal,Tunisia, and Togo.

42. Linton C. Freeman, “Centrality in Social Networks Conceptual Clarification,” Social Networks1, no. 3 (1979): 215–39.

43. Quan Zheng and David B. Skillicorn, “Spectral Embedding of Signed Networks,” in SIAMInternational Conference on Data Mining, 55–63 (2015), DOI 10.1137/1.9781611974010.7;Zheng, Skillicorn, and Walther, “Signed Directed Social Network Analysis Applied to GroupConflict” (see note 8 above).

44. Huitsing et al., “Univariate and Multivariate Models of Positive and Negative Networks” (seenote 35 above).

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45. Steven Radil, “Toward a Network Theory of the Diffusion of ‘New Wars,’” Paper presented atthe DASTI workshop on transnational extremist organizations, Rutgers University, September19–20, 2016.

46. Williams, War and Conflict in Africa (see note 13 above).47. Giuseppe Labianca and Daniel J. Brass, “Exploring the Social Ledger: Negative Relationships

and Negative Asymmetry in Social Networks in Organizations,” Academy of ManagementReview 31, no. 3 (2006): 596–614.

48. Smith et al., “Power in Politically Charged Networks” (see note 39 above).49. Baba Dakono, “Who’s Who in Northern Mali?,” ISS, Institute for Security Studies (2013),

https://www.issafrica.org/iss-today/whos-who-in-northern-mali.50. Details of the mathematical constructions can be found in Quan Zheng and David B.

Skillicorn, Social Networks with Rich Edge Semantics (Boca Raton, FL: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2017).

ORCID

Olivier Walther http://orcid.org/0000-0002-5768-2845Christian Leuprecht http://orcid.org/0000-0001-9498-4749David B. Skillicorn http://orcid.org/0000-0003-0605-4029

Appendix 1. Violent political organizations, 1997–2014

Abu Obeida BrigadeAbu Salim Martyrs’ BrigadeAl QaedaAl Qaqa BrigadeAl-Burayqah Martyr’s BrigadeAl-SalafiyaAl JihadiaAnsar al-ShariaAnsar DineAnsaruAQIM: Al Qaeda in the Islamic MaghrebBoko HaramBrega Martyrs BrigadeEl-Farouk BrigadeFalcons for the Liberation of AfricaFebruary 17 Martyrs BrigadeFighters of The Martyrs BrigadeFIS: Islamic Salvation FrontGIA: Armed Islamic GroupGMA: Mourabitounes Group of AzawadGSL: Free Salafist GroupGSPC: Salafist Group for Preaching and CombatIslamic Emirate of BarqaIslamic State of TripoliKnights of ChangeLibya Shield BrigadeLIDD: The Islamic League for Preaching and Holy StruggleMartyrs BrigadeMUJAO: Movement for Unity and Jihad in West AfricaMuslim Brotherhood

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Nawasi BrigadeNusur al-Sahel BrigadeRafallah Sehati BrigadeSoldiers of the Caliphate in AlgeriaThose Who Signed in BloodTimizart Brigade

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 0.00Military Forces of Libya 0.43 0.50 0.07MUJAO 0.32 0.84 0.53Military Forces of Algeria 0.28 0.50 0.21Protesters (Mali) 0.21 0.21 0.00Protesters (Libya) 0.19 0.19 0.00GIA 0.14 0.31 0.17Military Forces of Tunisia 0.14 0.14 0.00Police Forces of Tunisia 0.13 0.13 0.00Unidentified Armed Group (Algeria) 0.11 0.11 0.00Civilians (France) 0.11 0.26 0.15GMA 0.10 0.16 0.06Libya Shield Brigade 0.09 0.54 0.45Military Forces of Chad 0.09 0.12 0.04Military Forces of Libya Special Forces 0.06 0.13 0.06Libyan Rebel Forces 0.06 0.27 0.20LIDD 0.06 0.09 0.03GSPC 0.05 0.31 0.26AQIM 0.05 0.76 0.71Military Forces of Nigeria 0.04 0.09 0.05Wershefana Communal Militia (Libya) 0.03 0.14 0.10GLD 0.01 0.04 0.04Boko Haram 0.01 0.19 0.18Ansar Dine 0.00 0.42 0.42El-Farouk Brigade 0.00 0.06 0.06Military Forces of Mali 0.00 0.22 0.22MNLA 0.00 0.23 0.23Military Forces of France 0.00 0.25 0.25Those Who Signed in Blood −0.01 0.09 0.09Martyrs Brigade −0.02 0.04 0.06February 17 Martyrs Brigade −0.02 0.16 0.18Patriot Militia of Algerian Government −0.03 0.12 0.15Abu Salim Martyrs Brigade −0.04 0.00 0.04Civilians (Nigeria) −0.04 0.05 0.09Military Forces of Mauritania −0.05 0.05 0.10Military Forces of Niger −0.07 0.10 0.17Al Qaqa Brigade −0.07 0.00 0.07Ansaru −0.08 0.04 0.12Police Forces of Algeria −0.08 0.13 0.21FIS −0.08 0.00 0.08Al Qaeda −0.10 0.21 0.31Unidentified Armed Group (Libya) −0.10 0.37 0.48Civilians (Libya) −0.12 0.09 0.21Police Forces of Morocco −0.12 0.00 0.12Civilians (Niger) −0.12 0.00 0.12Civilians (Algeria) −0.12 0.08 0.21Rafallah Sehati Brigade −0.13 0.00 0.13UN −0.14 0.07 0.21Zawia Ethnic Militia (Libya) −0.16 0.00 0.16Civilians (Morocco) −0.16 0.00 0.16Civilians (International) −0.20 0.04 0.24Soldiers of the Caliphate in Algeria −0.21 0.00 0.21Civilians (Mali) −0.36 0.00 0.36Ansar al-Sharia −0.52 0.53 1.05Muslim 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, inaggression is the average length of incoming attack ties, and net aggression is the difference between the two.

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Appendix 3. Aggression levels for Libyan organizations.Group aggression out aggression in aggression

Protesters 1.24 1.24 0.00Military Forces 0.77 0.83 0.07Libyan Rebel Forces 0.41 0.68 0.28Libya Shield Brigade 0.36 1.13 0.77Wershefana Communal Militia 0.20 0.51 0.31Misratah Communal Militia 0.00 0.32 0.32Abu Salim Martyrs Brigade 0.00 0.06 0.06Ansar al-Sharia −0.04 0.70 0.74Islamist Militia −0.06 0.00 0.06Brega Martyrs Brigade −0.06 0.00 0.06Vigilante Militia −0.07 0.00 0.07Al Qaeda −0.09 0.00 0.09Al Qaqa Brigade −0.11 0.85 0.96Zintan Ethnic Militia −0.22 0.00 0.22Operation Libya Dawn −0.23 0.00 0.23Zawia Ethnic Militia −0.25 0.00 0.25Civilians −0.26 0.22 0.48Gharyan Communal Militia −0.30 0.00 0.30February 17 Martyrs Brigade −0.47 0.06 0.53Rafallah Sehati Brigade −0.81 0.00 0.81

Source: ACLED. Calculations: authors. Note: the following organizations with entirely zero rows have been removed fromthe table: El-Farouk Brigade, Al-Sawaiq Battalion, BSRC, Journalists, Police Forces, Salafist Group, Mutiny of MilitaryForces, Janzur Communal Militia, Awlad Suleiman Ethnic Militia, Shura Council of Benghazi Revolutionaries.

Appendix 4. Aggression levels for Nigerian organizations.Group aggression out aggression in aggression

Boko Haram 1.08 3.51 2.43Martyrs Brigade 0.98 0.98 0.00Ansaru 0.72 1.09 0.36Shuwa Ethnic Militia 0.48 0.48 0.00Lassa Communal Militia 0.47 0.47 0.00Kawuri Communal Militia 0.42 0.42 0.00Military Forces 0.36 0.41 0.04Attagara Communal Militia 0.32 0.32 0.00Civilian Joint Task Force 0.04 0.04 0.00Borno Vigilance Youths Group 0.04 0.04 0.00Unidentified Armed Group 0.02 0.02 0.00Civilians (Lebanon) 0.00 0.00 0.00Police Forces 0.00 0.04 0.04Vigilante Militia 0.00 0.04 0.04Militia (Ali Kwara) 0.00 0.00 0.00Fulani Ethnic Group 0.00 0.00 0.00All Progressives Congress 0.00 0.00 0.00VGN: Vigilante Group 0.00 0.48 0.48Shiite Muslim Group 0.00 0.00 0.00Civilians (China) −0.03 0.00 0.03UN −0.04 0.00 0.04Christian Militia −0.31 0.00 0.31Civilians (International) −0.57 0.00 0.57Military Forces Joint Task Force −0.64 0.00 0.64Civilians (South Korea) −0.70 0.00 0.70Private Security Forces −0.78 0.00 0.78Civilians (Europe) −0.89 0.00 0.89Civilians −0.98 0.04 1.02

Source: ACLED. Calculations: authors. Note: the following organizations with entirely zero rows have been removed fromthe table: ANPP, Prison Guards, Muslim Group, Christian Group, Students, PDP, Government, Military Forces UK, IgboEthnic Group.

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