Untangling the network effects of productivity and prominence among scientists.

While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, we develop models that untangle the network effects of productivity defined as paper counts, and prominence referring to high-impact publications, of individual scientists from their collaboration networks. We find that gendered differences in the productivity and prominence of mid-career researchers can be largely explained by differences in their coauthorship networks.

Functional connectivity signatures of political ideology.

Emerging research has begun investigating the neural underpinnings of the biological and psychological differences that drive political ideology, attitudes, and actions. Here, we explore the neurological roots of politics through conducting a large sample, whole-brain analysis of functional connectivity (FC) across common fMRI tasks. Using convolutional neural networks, we develop predictive models of ideology using FC from fMRI scans for nine standard task-based settings in a novel cohort of healthy adults ( = 174, age range: 18 to 40, mean = 21.

Understanding the Relationship Between Official and Social Information About Infectious Disease: Experimental Analysis.

Background: Communicating official public health information about infectious diseases is complicated by the fact that individuals receive much of their information from their social contacts, either via interpersonal interaction or social media, which can be prone to bias and misconception.

Objective: This study aims to evaluate the effect of public health campaigns and the effect of socially communicated health information on learning about diseases simultaneously. Although extant literature addresses the effect of one source of information (official or social) or the other, it has not addressed the simultaneous interaction of official information (OI) and social information (SI) in an experimental setting.

Exploring the direct and indirect effects of elite influence on public opinion.

Political elites both respond to public opinion and influence it. Elite policy messages can shape individual policy attitudes, but the extent to which they do is difficult to measure in a dynamic information environment. Furthermore, policy messages are not absorbed in isolation, but spread through the social networks in which individuals are embedded, and their effects must be evaluated in light of how they spread across social environments.

Dynamics of social network emergence explain network evolution.

Networked systems emerge and subsequently evolve. Although several models describe the process of network evolution, researchers know far less about the initial process of network emergence. Here, we report temporal survey results of a real-world social network starting from its point of inception.

Tightly Bound: The Relationship of Network Clustering Coefficients and Reincarceration at Three Therapeutic Communities.

Objective: Clustering, the tendency of individuals to form closed triads, is ubiquitous in human social networks. Previous research has found that therapeutic community (TC) residents whose social networks include a high degree of clustering are less likely to be reincarcerated following discharge. In this study, we test this finding in a larger number of TCs.

Elusive consensus: Polarization in elite communication on the COVID-19 pandemic.

Cues sent by political elites are known to influence public attitudes and behavior. Polarization in elite rhetoric may hinder effective responses to public health crises, when accurate information and rapid behavioral change can save lives. We examine polarization in cues sent to the public by current members of the U.

Building the community: Endogenous network formation, homophily and prosocial sorting among therapeutic community residents.

Background: Researchers have begun to consider the ways in which social networks influence therapeutic community (TC) treatment outcomes. However, there are few studies of the way in which the social networks of TC residents develop over the course of treatment.

Methodology: We used a Temporal Exponential Random Graph Model (TERGM) to analyze changes in social networks totaling 320,387 peer affirmations exchanged between residents in three correctional TCs, one of which serves men and two of which serve both men and women.

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.

A consistent organizational structure across multiple functional subnetworks of the human brain.

A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attention. Understanding how regions in these subnetworks relate is thus crucial to understanding the emergence of cognitive processes. However, the organizing principles that guide how regions within subnetworks communicate, and whether there is a common set of principles across subnetworks, remains unclear.

Latent influence networks in global environmental politics.

International environmental treaties are the key means by which states overcome collective action problems and make specific commitments to address environmental issues. However, systematically assessing states' influence in promoting global environmental protection has proven difficult. Analyzing newly compiled data with a purpose-built statistical model, we provide a novel measurement of state influence within the scope of environmental politics and find strong influences among states and treaties.

Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure.

We investigate the functional organization of the Default Mode Network (DMN) - an important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure.

A three-degree horizon of peace in the military alliance network.

States form defensive military alliances to enhance their security in the face of potential or realized interstate conflict. The network of these international alliances is increasingly interconnected, now linking most of the states in a complex web of ties. These alliances can be used both as a tool for securing cooperation and to foster peace between direct partners.

Kantian fractionalization predicts the conflict propensity of the international system.

Network science has spurred a reexamination of relational phenomena in political science, including the study of international conflict. We introduce a new direction to the study of conflict by showing that the multiplex fractionalization of the international system along three key dimensions is a powerful predictor of the propensity for violent interstate conflict. Even after controlling for well-established conflict indicators, our new measure contributes more to model fit for interstate conflict than all of the previously established measures combined.

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.

Statistical inference for valued-edge networks: the generalized exponential random graph model.

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges.