Social network analysis is well-known as a fundamental tool for structural assessment of relationships at scale. The overwhelming convention in this field is to model relations based on positive factors, often related to commonality between participants, such as mutual friendship, or other characteristics that may positively bind participants. However, this leaves undefined the meaning of absent links between agents, leaving open scope for alternative interpretations. For example, an absent link may be due to individuals being unfamiliar and irrelevant to each other, but it could also be due to negative relations. This context is particularly relevant to adversarial scenarios, where actors, or the groups that they represent, are the focus of analysis. Accordingly, in this paper we consider the importance of explicitly modelling so-called negative ties as a means to provide insight into conflict and adversarial interaction. This subtle redefinition of social network analysis provides an opportunity to gain new perspectives on the threats that can subvert traditional social network analysis due to negativity not being explicitly represented. In this paper we focus on the fundamental issue of how to define negative ties. Generally, these invoke or are the consequence of cognitive dissonance between conflicting parties. We distinguish further between these three domains of negative, network connections and provide illustrations of each type in the context of social conflict. We discuss implications of these various genres of negativity regarding their potential to be used as tools by adversaries and their ability to provide a deeper understanding of human conflict.
One of the key factors affecting any multi-domain operation concerns the influence of unorganized militias, which may often counter a more advanced adversary by means of terrorist incidents. In order to ensure the achievement of strategic objectives, the actions and influence of such violent activities need to be taken into account. However, in many cases, full information about the incidents that may have affected civilians and non-government organizations is hard to determine. In the situation of asymmetric warfare, or when planning a multi-domain operation, often the identity of the perpetrator may not themselves be known. In order to support a coalition commander's mandate, one could use AI/ML techniques to provide the missing details about incidents in the field which may only be partially understood or analyzed. In this paper, we examine the goal of predicting the identity of the perpetrator of a terrorist incident using AI/ML techniques on historical data, and discuss how well the AI/ML models can work to help clean the data available to the commander for data analysis.
Network structure represents a vital component in wide-ranging aspects of Multi-Domain Operations (MDO). One specific type of network that holds promise in understanding the behavior of complex environments such as MDO consists of ones where nodes are combined with both positive ties and negative ties. Positive ties are edges that promote nodes to become similar to each other, or homophilous, while negative ties are edges that promote nodes to be dissimilar to each other. Such a model of influence among the nodes can be used to explain various phenomena happening within a society, modeling peer influences, spread of memes, or to model incidents of violence. In this paper, we propose a Positive-Negative tie network model to analyze terrorism incidents in India, and investigate the role of this network in general network classification and situation understanding contexts.
KEYWORDS: Social networks, Analytical research, Data modeling, Algorithm development, Network security, Network architectures, Statistical analysis, Information science, Systems modeling, Web 2.0 technologies
Within networks one can identify motifs that are significant recurring patterns of interaction between nodes. Here motifs are sub-graphs that occur more frequently than would be explained by random connections. Graphs can be used to model internal network structures of human groups, or links between groups, with group dynamics being governed by these structures. Graphs can also model behavior in engineered systems, and internal network structures can significantly affect dynamic behavior. A graph may only be partially visible (such as in hostile or coalition environments), however detectable network motifs may in some cases be reflective of the entire graph. We outline a research plan and describe basic network motifs and their properties, along with current analytic techniques for static and dynamic settings. We offer suggestions as to how network motif techniques can be applied to intra- or inter- group behavior, for example to detect whether multiple groups behave as a co-operative alliance, or whether coalition networks inter-operate in positive ways. As an example, we examine a complex time-series graph dataset relevant to coalition focused aspects of the class of networks under study, specifically related to the social network resulting from the authorship of academic papers within a coalition. We provide details of the basic analysis of this network over time and outline how this can be used as one of the datasets for our planned network motif research activities, especially with regards to the temporal and evolutionary aspects.
A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations
Over the last 70 years there has been a major shift in the threats to global peace. While the 1950’s and 1960’s were characterised by the cold war and the arms race, many security threats are now characterised by group behaviours that are disruptive, subversive or extreme. In many cases such groups are loosely and chaotically organised, but their ideals are sociologically and psychologically embedded in group members to the extent that the group represents a major threat. As a result, insights into how human groups form, emerge and change are critical, but surprisingly limited insights into the mutability of human groups exist. In this paper we argue that important clues to understand the mutability of groups come from examining the evolutionary origins of human behaviour. In particular, groups have been instrumental in human evolution, used as a basis to derive survival advantage, leaving all humans with a basic disposition to navigate the world through social networking and managing their presence in a group. From this analysis we present five critical features of social groups that govern mutability, relating to social norms, individual standing, status rivalry, ingroup bias and cooperation. We argue that understanding how these five dimensions interact and evolve can provide new insights into group mutation and evolution. Importantly, these features lend themselves to digital modeling. Therefore computational simulation can support generative exploration of groups and the discovery of latent factors, relevant to both internal group and external group modelling. Finally we consider the role of online social media in relation to understanding the mutability of groups. This can play an active role in supporting collective behaviour, and analysis of social media in the context of the five dimensions of group mutability provides a fresh basis to interpret the forces affecting groups.