Kantian fractionalization predicts the conflict propensity of the international system

The study of complex social and political phenomena with the perspective and methods of network science has proven fruitful in a variety of areas, including applications in political science and more narrowly the field of international relations. We propose a new line of research in the study of international conflict by showing that the multiplex fractionalization of the international system (which we label Kantian fractionalization) is a powerful predictor of the propensity for violent interstate conflict, a key indicator of the system's stability. In so doing, we also demonstrate the first use of multislice modularity for community detection in a multiplex network application. Even after controlling for established system-level conflict indicators, we find that Kantian fractionalization contributes more to model fit for violent interstate conflict than previously established measures. Moreover, evaluating the influence of each of the constituent networks shows that joint democracy plays little, if any, role in predicting system stability, thus challenging a major empirical finding of the international relations literature. Lastly, a series of Granger causal tests shows that the temporal variability of Kantian fractionalization is consistent with a causal relationship with the prevalence of conflict in the international system. This causal relationship has real-world policy implications as changes in Kantian fractionalization could serve as an early warning sign of international instability.Comment: 17 pages + 17 pages designed as supplementary online materia

Infectivity Enhances Prediction of Viral Cascades in Twitter

Models of contagion dynamics, originally developed for infectious diseases, have proven relevant to the study of information, news, and political opinions in online social systems. Modelling diffusion processes and predicting viral information cascades are important problems in network science. Yet, many studies of information cascades neglect the variation in infectivity across different pieces of information. Here, we employ early-time observations of online cascades to estimate the infectivity of distinct pieces of information. Using simulations and data from real-world Twitter retweets, we demonstrate that these estimated infectivities can be used to improve predictions about the virality of an information cascade. Developing our simulations to mimic the real-world data, we consider the effect of the limited effective time for transmission of a cascade and demonstrate that a simple model for slow but non-negligible decay of the infectivity captures the essential properties of retweet distributions. These results demonstrate the interplay between the intrinsic infectivity of a tweet and the complex network environment within which it diffuses, strongly influencing the likelihood of becoming a viral cascade.Comment: 16 pages, 10 figure

The Impact of Hispanic and White Group Cues on Attitudes Towards the Violation of Generic Norms

While much work in political science has examined the impact of racial cues on individual perceptions, we know little about how individuals evaluate members of minority outgroups on issues that are not linked to stereotypes. We measure the impacts of Hispanic and White cues on individual assessments related to a stereotype-independent norm violation: alcoholism. We test three competing theories – cognition, intergroup emotions, and social identity – using a population-based vignette experiment included in the General Social Survey. Our results contradict much of the literature, but keep with social identity theory's predictions. Hispanic alcoholics, when Hispanics constitute the outgroup, are assessed less negatively than White alcoholics in the ingroup, the latter experiencing what is called the black sheep effect. The black sheep effect occurs when ingroup members are more punitive towards members of the ingroup than the outgroup. However, the black sheep effect does not extend to measures that are more consistent with outgroup stereotypes, such as violence or money mismanagement; Hispanic alcoholics are evaluated more negatively than Whites on these measures. The implication is that the effect of racial cues depends strongly on issue linkages to group stereotypes

Stochastic Weighted Graphs: Flexible Model Specification and Simulation

In most domains of network analysis researchers consider networks that arise in nature with weighted edges. Such networks are routinely dichotomized in the interest of using available methods for statistical inference with networks. The generalized exponential random graph model (GERGM) is a recently proposed method used to simulate and model the edges of a weighted graph. The GERGM specifies a joint distribution for an exponential family of graphs with continuous-valued edge weights. However, current estimation algorithms for the GERGM only allow inference on a restricted family of model specifications. To address this issue, we develop a Metropolis--Hastings method that can be used to estimate any GERGM specification, thereby significantly extending the family of weighted graphs that can be modeled with the GERGM. We show that new flexible model specifications are capable of avoiding likelihood degeneracy and efficiently capturing network structure in applications where such models were not previously available. We demonstrate the utility of this new class of GERGMs through application to two real network data sets, and we further assess the effectiveness of our proposed methodology by simulating non-degenerate model specifications from the well-studied two-stars model. A working R version of the GERGM code is available in the supplement and will be incorporated in the gergm CRAN package.Comment: 33 pages, 6 figures. To appear in Social Network