We investigate signed networks with community structure with respect to their spectra and their evolution under a dynamical model of structural balance, a prominent theory of signed social networks. The spectrum of the adjacency matrix generated by a stochastic block model with two equal-size communities shows detectability transitions in which the community structure becomes manifest when its signal eigenvalue appears outside the main spectral band. The spectrum also exhibits "sociality" transitions involving the homogeneous structure representing the average tie value. We derive expressions for the eigenvalues associated with the community and homogeneous structure as well as the transition boundaries, all in good agreement with numerical results. Using the stochastically generated networks as initial conditions for a simple model of structural balance dynamics yields three outcome regimes: two hostile factions that correspond with the initial communities, two hostile factions uncorrelated with those communities, and a single harmonious faction of all nodes. The detectability transition predicts the boundary between the assortative and mixed two-faction states and the sociality transition predicts that between the mixed and harmonious states. Our results may yield insight into the dynamics of cooperation and conflict among actors with distinct social identities.

Violent conflict among rebels is a common feature of civil wars and insurgencies. Yet, not all rebel groups are equally prone to such infighting. While previous research has focused on the systemic causes of violent conflict within rebel movements, this article explores the factors that affect the risk of conflict between pairs of rebel groups. We generate hypotheses concerning how differences in power, ideology, and state sponsors between rebel groups impact their propensity to clash and test them using data from the Syrian civil war. The data, drawn from hundreds of infighting claims made by rebel groups on social media, are used to construct a network of conflictual ties among 30 rebel groups. The relationship between the observed network structure and the independent variables is evaluated using network analysis metrics and methods including assortativity, community structure, simulation, and latent space modeling. We find strong evidence that ideologically distant groups have a higher propensity for infighting than ideologically proximate ones. We also find support for power asymmetry, meaning that pairs of groups of disparate size are at greater risk of infighting than pairs of equal strength. No support was found for the proposition that sharing state sponsors mitigates rebels’ propensity for infighting. Our results provide an important corrective to prevailing theory, which discounts the role of ideology in militant factional dynamics within fragmented conflicts.

We present a model describing the temporal evolution of opinions due to interactions among a network of individuals. This Accept-Shift-Constrict (ASC) model is formulated in terms of coupled nonlinear differential equations for opinions and uncertainties. The ASC model dynamics allows for the emergence and persistence of majority positions so that the mean opinion can shift even for a symmetric network. The model also formulates a distinction between opinion and rhetoric in accordance with a recently proposed theory of the group polarization effect. This enables the modeling of discussion-induced shifts toward the extreme without the typical modeling assumption of greater resistance to persuasion among extremists. An experiment is described in which triads engaged in online discussion. Simulations show that the ASC model is in qualitative and quantitative agreement with the experimental data.

When rebels make alliances, what informs their choice of allies? Civil wars are rarely simple contests between rebels and incumbent regimes. Rather, rival militant networks provide the context in which these fragmented conflicts unfold. Alliances that emerge within this competitive landscape have the power to alter conflict trajectories and shape their outcomes. Yet patterns of interrebel cooperation are understudied. The existing scholarship on rebel alliances focuses on why rebels cooperate, but little attention is given to the composition of those alliances: with whom rebels cooperate. We explore how power, ideology, and state sponsorship can shape alliance choices in multiparty civil wars. Employing network analysis and an original data set of tactical cooperation among Syrian rebels, we find compelling evidence that ideological homophily is a primary driver of rebel collaboration. Our findings contribute to an emerging literature that reasserts the role of ideology in conflict processes.

We present a novel explanation for the group polarization effect whereby discussion among like-minded individuals induces shifts toward the extreme. Our theory distinguishes between a quantitative policy under debate and the discussion’s rhetorical frame, such as the likelihood of an outcome. If policy and frame position are mathematically related so that frame position increases more slowly as the policy becomes more extreme, majority formation at the extreme is favored, thereby shifting consensus formation toward the extreme. Additionally, use of a heuristic frame can shift the frame reference point away from the policy reference, yielding differential polarization on opposing policy sides. We present a mathematical model that predicts consensus policy given group member initial preferences and network structure. Our online group discussion experiment manipulated policy side, disagreement level, and network structure. The results, which challenge existing polarization theory, are in qualitative and quantitative accord with our theory and model.

This chapter describes a quantitative methodology for the analysis and modelling of leadership networks which leverages research in complex systems, mainly nonlinear dynamical systems theory and network science. A prototype software package, PORTEND, is introduced implementing the methodology and using data from expert analysts to assess policy and factional outcomes with respect to the internal dynamics of a system of political actors. The methodology includes structural analysis methods, such as algorithms for analysing issue positions and community structure, and a simulation of nonlinear social influence dynamics. PORTEND's capabilities are illustrated for an application to Iran involving fifteen leadership elites and seven issues. The factional structure of the Iranian leadership group is analysed first based on their issue positions, then with respect to the network of inter-actor influence relationships, and finally by a synthesis of the issue and network data. An application of the nonlinear social influence simulation to the nuclear issue is presented and its implications are discussed with respect to Iranian decision-making concerning the 2013–2015 nuclear negotiations. Publication of record: https://www.elgaronline.com/view/9781785364419.00033.xml

Research using social network analysis to study terrorism and insurgency has increased dramatically following the 9/11 attacks against the United States. This research emphasizes the importance of relational analysis and provides a variety of concepts, theories, and analytical tools to better understand questions related to militant group behavior and outcomes of terrorism and insurgent violence. This paper defines key network concepts, identifies important network metrics, and reviews theoretical and empirical research on network analysis and militant groups. We find that the main focus of existing research is on organizational analysis and its implications for militant group operational processes and performance. Few studies investigate how differences in network structure lead to divergent outcomes with respect to political processes such as militant group infighting, their strategic use of violence, or how politically salient variables affect the evolution of militant cooperative networks. Consequently, we propose a research agenda aimed at using network analysis to investigate the political interactions of militant groups within a single conflict and provide illustrations on how to pursue this agenda. We believe that such research will be of particular value in advancing the understanding of fragmented civil wars and insurgencies consisting of multiple, independent militant groups.

Using a nonlinear model of opinion dynamics on networks, we show the existence of asymmetric majority rule solutions for symmetric initial opinion distributions and symmetric network structure. We show that this occurs in triads as the result of a pitchfork bifurcation and arises in both chain and complete topologies with symmetric as well as asymmetric coupling. Analytical approximations for bifurcation boundaries are derived which closely match numerically-obtained boundaries. Bifurcation-induced symmetry breaking represents a novel mechanism for generating majority rule outcomes without built-in structural or dynamical asymmetries; however, the policy outcome is fundamentally unpredictable.

Terrorism scholarship has revealed the importance of small groups—both cells and leadership groups—in the proliferation of violence, yet this field remains only loosely connected to small group theory and research. There exists no systematic consideration of the role that group dynamics play in the disruption of terrorist activities. This article proposes an analytical framework for terrorist group disruption that shows how the goals and methods of counterterrorist intervention intersect with small group behavior. We use this framework to theorize how three intervention types—repression, manipulation, and persuasion—interact with group variables and processes, such as communication networks, social identities, group cohesion, and intragroup conflict. Seven theoretical propositions demonstrate how the framework can show how the direct and indirect effects of group behavior can augment or undermine counterterrorist strategies.

A methodology for modeling group decision making by political elites is described and its application to real-world contexts is illustrated for the case of Afghanistan. The methodology relies on the judgments of multiple experts as input and can improve analysis of political decision making by elucidating the factional structure of the group of elites and simulating their interaction in a policy debate. This simulation is performed using a model of small group decision making which integrates actor policy preferences and their inter-relationship network within a nonlinear dynamical systems theory framework. In addition to the basic nonlinear model, various components required to implement the methodology are described such as the analyst survey, structural analysis, and simulation. Implementation and analysis results are discussed for both the government and insurgent sides of the current conflict in Afghanistan.

This article first considers the Rabbani assassination and finds it unlikely that it was conducted by "spoilers" distinct from the Taliban central leadership. The killing of the head of Afghan reconciliation efforts with insurgents is on its face a Taliban rejection of negotiations with the Afghan government, the only party with whom a meaningful peace settlement can be made. Analysis of his position within the network of government elites, however, suggests that the Taliban's overriding motive in targeting Rabbani was not to punctuate a rejection of peace talks, but rather to exacerbate ethnic cleavages within the Afghan government, thereby weakening its cohesion and isolating President Hamid Karzai. Chipping away at Afghan national unity is a companion political strategy in service of the military objective of weakening the ANSF who, given the U.S. drawdown, will form the primary obstacle to Taliban control of either the Pashtun belt in the south and east or the country as a whole.

We investigate a multi-task approach to similarity discriminant analysis, where we propose treating the estimation of the different class-conditional distributions of the pairwise similarities as multiple tasks. We show that regularizing these estimates together using a least-squares regularization weighted by a task-relatedness matrix can reduce the resulting maximum a posteriori classification errors. Results are given for benchmark data sets spanning a range of applications. In addition, we present a new application of similarity-based learning to analyzing the rhetoric of multiple insurgent groups in Iraq. We show how to produce the necessary task relatedness information from standard given training data, as well as how to derive task-relatedness information if given side information about the class relatedness.

Although the fundamental importance of the political dimension of insurgencies is widely accepted, there has been little effort to develop a dedicated theory of what is the most central political observable of insurgents - their rhetoric. We propose a theoretical framework that addresses three key features of insurgent rhetoric: (1) ideologies as represented by "conflict frames," which consist of the in-groups and out-groups in insurgent discourse; (2) operational claims of attacks; and (3) publicly declared tactical cooperation with other insurgent groups. Quantitative representations of these features are constructed and empirically implemented on the rhetoric of Iraqi insurgent groups from 2003-2009. Our framework considers a group's conflict frame as a form of master organizing logic by which the group seeks the conflict to be understood; a group's operational claims are a way of establishing its action-oriented character and serve to reinforce its conflict frame. Accordingly, we expect that group conflict frames should be broadly consistent with their targeting portfolios - that is, the types of target classes which they claim to attack. This hypothesis is quantitatively supported by significant correlations between conflict frame and targeting portfolio variables. Additionally, in situations of intense factional competition, the base of active insurgency supporters is a prime audience for insurgent rhetoric, and so we also contend that a group's conflict frame should reflect the social identities of its desired constituency among the active base. This hypothesis is supported by correlation of group conflict frames with structure in the network of declared joint operations between groups. Our research demonstrates the ability to meaningfully quantify insurgent rhetoric and the importance of rhetoric as a tool for revealing insight into insurgent factional structure and dynamics.

Insurgent rhetoric is often treated dismissively as propaganda. This paper assumes an alternative perspective in which insurgent rhetoric is considered as sophisticated political rhetoric, capable of revealing meaningful insight into the internal structure of an insurgency especially under conditions of intense factional competition. Using a quantitative approach, we demonstrate that this is indeed the case using rhetoric of Iraqi Sunni insurgent groups. Our focus is on insurgent operational claims and, in particular, the types of target classes that insurgents claim to attack. We contend that variations in the claimed target classes should reflect differences in group composition among the base of active insurgency supporters. A theoretical framework is set forth in which insurgent operational claims and identity rhetoric are related to concepts from Aristotelian rhetoric theory, modern social identity theory, and framing theory. Several hypotheses following from this framework are proposed. We construct a quantitative "targeting policy" variable which gauges the perceived legitimacy of the portfolio of target classes claimed by a group. Targeting policy differences are seen to correspond with ideological differences. We also construct networks from insurgent rhetoric, in particular, claims of joint operations and joint communiques, indicative of insurgent group relationships at the foot soldier and leadership levels respectively. Correlations between targeting policy and the joint operations network structure support our argument that targeting claims are an indicator of social identities among a group's active support constituencies.

Insurgent operational claims in Iraq are analyzed from the perspective that such claims can help illuminate the internal structure of an insurgency, especially in situations of intense factional competition between rival groups. Our primary focus is on the types of target classes that different insurgent groups claim to attack and claims of joint operations between insurgent groups. The former allows us to construct a targeting policy variable and the latter enables us to construct a network of relationships between insurgent groups, particularly at the foot soldier level. We contend that variations in the claimed target classes should reflect differences in group composition. Correlations between targeting policy and the joint operations network structure support this hypothesis.

We also present a model of insurgent cooperation with respect to joint operations. In addition to a homophily process driven by targeting policy proximity, the model posits both a horizontal interaction mechanism – that foot soldier units can initiate joint operations with units from other insurgent groups – and a hierarchical one in which the network linking the leaderships of insurgent groups also plays a role. Simulation results indicate a good ability to match the structure of the observed networks.