Measuring influence and determining what drives it are persistent questions in political science and in network analysis more generally. Herein we focus on the domain of international relations. Our major substantive question is: How can we determine what characteristics make an actor influential? To address the topic of influence, we build on a multilinear tensor regression framework (MLTR) that captures influence relationships using a tensor generalization of a vector autoregression model. Influence relationships in that approach are captured in a pair of n x n matrices and provide measurements of how the network actions of one actor may influence the future actions of another. A limitation of the MLTR and earlier latent space approaches is that there are no direct mechanisms through which to explain why a certain actor is more or less influential than others. Our new framework, social influence regression, provides a way to statistically model the influence of one actor on another as a function of characteristics of the actors. Thus we can move beyond just estimating that an actor influences another to understanding why. To highlight the utility of this approach, we apply it to studying monthly-level conflictual events between countries as measured through the Integrated Crisis Early Warning System (ICEWS) event data project.

The growing importance of China in global politics cannot be understated. What makes the country's rise even more interesting is that in the last two the United States has been involved in a pair of wars in the Middle East. These actions required large commitments, not just of blood and treasure, but also of attention and resolve. We examine how third parties respond to the United State's focus on conflicts in one region of the world, in terms of their cooperation in the economic and diplomatic spheres. To do so, we develop a novel measure of US distraction, as well as novel network based measures of economic and diplomatic cooperation or alignment. We seek to test the theory that when the US is more distracted, other states will be more likely to cooperate with the United States' principal rival, China. We find that such cooperation is more likely in the diplomatic sphere than the economic one. However, when we look at how different types of states react to US distraction, we find that across both measures democracies generally respond by moving farther away from China, while non-democracies move closer to China.

Understanding the relationship between trade and conflict has always been important but it has never been more pressing in the wake of rising trade tensions between the today's most powerful economies. In this paper, we break new ground by first, examining the role cooperation plays in explaining the relationship between trade and conflict across different theoretical frameworks (liberal, realist and bargaining). Second, by exploring how both verbal and material forms of conflict and cooperation affect trade outcomes, we are further able to test at least one possible pathway through which trade and conflict affect one another. Finally, by using multilinear tensor regression analysis, we account for problems with endogeneity and higher level dependencies (e.g. reciprocity and transitivity) that the scholarship has long struggled to address. We find evidence for a realist understanding of the relationship between trade and conflict: trade asymmetries and verbal and material conflict simultaneously affect each other.

Today civil conflict is characterized by multiple warring parties with different motivations, capabilities, and patterns of behavior. Current research on intrastate conflict focuses heavily on questions related to the military behavior of these armed actors and battlefield dynamics---such as in studies on the efficacy of counterinsurgency campaigns, the causes of civilian victimization, or conflict duration. We argue that despite this focus on battle level processes, political science lacks a clear, generalizable measure of offensive and defensive battlefield behavior to capture which actors initiate conflict at the micro-level. Although significant advances have been made in the collection of event data on intrastate conflict, currently available event data does not record information about which actor initiates a clash. Instead, these data only record which two (or more) actors engage in a battle at any given time. We present a novel latent variable framework that parses directionality from event data via a network decomposition approach. We apply this approach to the Nigerian Civil conflict to investigate the relationship between directionality and civilian victimization. We run our model across all conflict based events recorded in Nigeria from 1997 to 2019 and evaluate how civilian victimization (in terms of both intensity and tactics) changes as a function of whether or not the government is on the defensive or offensive.