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The rise of China as a global power has been a prominent feature in international politics. Simultaneously, the United States has been engaged in ongoing conflicts in the Middle East and South Asia for the past two decades, requiring a significant commitment of resources, focus, and determination. This paper investigates how third-party countries react to the United States' preoccupation with these conflicts, particularly in terms of diplomatic cooperation and alignment. We introduce a measure of US distraction and utilize network-based indicators to assess diplomatic cooperation or alignment. Our study tests the hypothesis that when the US is distracted, other states are more likely to cooperate with its principal rival, China. Our findings support this hypothesis, revealing that increased cooperation with China is more probable during periods of US distraction. However, a closer examination of state responses shows that democracies generally distance themselves from China under these circumstances, while non-democracies move closer. 

Abstract Understanding network influence and its determinants are key challenges in political science and network analysis. Traditional latent variable models position actors within a social space based on network dependencies but often do not elucidate the underlying factors driving these interactions. To overcome this limitation, we propose the social influence regression (SIR) model, an extension of vector autoregression tailored for relational data that incorporates exogenous covariates into the estimation of influence patterns. The SIR model captures influence dynamics via a pair of$$n \times n$$matrices that quantify how the actions of one actor affect the future actions of another. This framework not only provides a statistical mechanism for explaining actor influence based on observable traits but also improves computational efficiency through an iterative block coordinate descent method. We showcase the SIR model’s capabilities by applying it to monthly conflict events between countries, using data from the Integrated Crisis Early Warning System (ICEWS). Our findings demonstrate the SIR model’s ability to elucidate complex influence patterns within networks by linking them to specific covariates. This paper’s main contributions are: (1) introducing a model that explains third-order dependencies through exogenous covariates and (2) offering an efficient estimation approach that scales effectively with large, complex networks. 

Abstract Building on recent developments in the literature, this article addresses a prominent research question in the study of civil conflict: what explains violence against civilians? We use a novel computational model to investigate the strategic incentives for victimization in a network setting; one that incorporates civilians’ strategic behavior. We argue that conflicts with high network competition—where conflict between any two actors is more likely—lead to higher rates of civilian victimization, irrespective of the conflict's overall intensity or total number of actors. We test our theory in a cross-national setting using event data to generate measures of both conflict intensity and network density. Empirical analysis supports our model's finding that conflict systems with high levels of network competition are associated with a higher level of violence against the civilian population. 

Abstract International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data. 

Abstract Spatial interdependencies commonly drive the spread of violence in civil conflict. To address such interdependence, scholars often use spatial lags to model the diffusion of violence, but this requires an explicit operationalization of the connectivity matrices that represent the spread of conflict. Unfortunately, in many cases, there are multiple competing processes that facilitate the spread of violence making it difficult to identify the true data-generating process. We show how a network-driven methodology can allow us to account for the spread of violence, even in the cases where we cannot directly measure the factors that drive diffusion. To do so, we estimate a latent connectivity matrix that captures a variety of possible diffusion patterns. We use this procedure to study intrastate conflict in eight conflict-prone countries and show how our framework enables substantially better predictive performance than canonical spatial-lag measures. We also investigate the circumstances under which canonical spatial lags suffice and those under which a latent network approach is beneficial.