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. Hence, collaboration networks act as a form of social capital, and we find evidence of their transferability from senior to junior collaborators, with benefits that decay as researchers age. Collaboration network effects can also explain a large proportion of the productivity and prominence advantages held by researchers at prestigious institutions. These results highlight a substantial role of social networks in driving inequalities in science, and suggest that collaboration networks represent an important form of unequally distributed social capital that shapes who makes what scientific discoveries.

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 (n = 174, age range: 18 to 40, mean = 21.43) from the Ohio State University Wellbeing Project. Our analyses suggest that liberals and conservatives have noticeable and discriminative differences in FC that can be identified with high accuracy using contemporary artificial intelligence methods and that such analyses complement contemporary models relying on socio-economic and survey-based responses. FC signatures from retrieval, empathy, and monetary reward tasks are identified as important and powerful predictors of conservatism, and activations of the amygdala, inferior frontal gyrus, and hippocampus are most strongly associated with political affiliation. Although the direction of causality is unclear, this study suggests that the biological and neurological roots of political behavior run much deeper than previously thought.

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. Using a sample of 358 participants across thirty student organizations at a large Midwestern research university, we experimentally investigate how real social groups consume and share elite information when evaluating a relatively unfamiliar policy area. We find a significant, direct effect of elite policy messages on individuals' policy attitudes. However, we find no evidence that policy attitudes are impacted indirectly by elite messages filtered through individuals' social networks. Results illustrate the power of elite influence over public opinion.

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. METHODS: We used a series of experiments that exposed participants to both OI and structured SI about the symptoms and spread of hepatitis C over a series of 10 rounds of computer-based interactions. Participants were randomly assigned to receive a high, low, or control intensity of OI and to receive accurate or inaccurate SI about the disease. RESULTS: A total of 195 participants consented to participate in the study. Of these respondents, 186 had complete responses across all ten experimental rounds, which corresponds to a 4.6% (9/195) nonresponse rate. The OI high intensity treatment increases learning over the control condition for all symptom and contagion questions when individuals have lower levels of baseline knowledge (all P values ≤.04). The accurate SI condition increased learning across experimental rounds over the inaccurate condition (all P values ≤.01). We find limited evidence of an interaction between official and SI about infectious diseases. CONCLUSIONS: This project demonstrates that exposure to official public health information increases individuals' knowledge of the spread and symptoms of a disease. Socially shared information also facilitates the learning of accurate and inaccurate information, though to a lesser extent than exposure to OI. Although the effect of OI persists, preliminary results suggest that it can be degraded by persistent contradictory SI over time.

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. We find that individuals' ties undergo an initial cycle of rapid expansion and contraction. This process helps to explain the eventual interactions and working structure in the network (in this case, scientific collaboration). We propose a stylized concept and model of "churn" to describe the process of network emergence and stabilization. Our empirical and simulation results suggest that these network emergence dynamics may be instrumental for explaining network details, as well as behavioral outcomes at later time periods.

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. METHOD: We use a temporal network autocorrelation model (TNAM) to analyze clustering in social networks of affirmations exchanged between TC residents as a predictor of the hazard of reincarceration. The networks were drawn from three corrections-based TCs, two of which include both men's and women's units and one of which housed only men. RESULTS: The findings were inconsistent across facilities. Increased clustering correlates with a reduced hazard of reincarceration for women at both facilities (ß = -3.274, 95% CI [-4.299, -2.238]; ß = -18.233, 95% CI [-32.370, -4.095]) and for men at two of the facilities (ß =-0.910, 95% CI [-1.213, -0.606]; ß = -1.393, 95% CI [-1.825, -0.961]). However, clustering increased the hazard of reincarceration for men at one facility (ß = 5.558, 95% CI [4.124, 6.993]). CONCLUSIONS: These results support the idea that the likelihood of reincarceration following discharge from a TC is predicted by clustering, a network structure that occurs at a system level between the individual resident and the entire community. Inconsistency in the direction of the relationship suggests that future research should analyze predictors of prosocial clustering in 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.S. House and Senate during the onset of the COVID-19 pandemic, measuring polarization as the ability to correctly classify the partisanship of tweets' authors based solely on the text and the dates they were sent. We find that Democrats discussed the crisis more frequently-emphasizing threats to public health and American workers-while Republicans placed greater emphasis on China and businesses. Polarization in elite discussion of the COVID-19 pandemic peaked in mid-February-weeks after the first confirmed case in the United States-and continued into March. These divergent cues correspond with a partisan divide in the public's early reaction to the crisis.

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. The networks were analyzed within weekly and monthly time-frames. RESULTS: Within a weekly time-frame residents tended to close triads. Residents who were not previously connected tended not to affirm the same peers. Residents showed homophily by entry cohort. Other results were inconsistent across TC units. Within a monthly time-frame participants showed homophily by graduation status. They showed the same patterns of triadic closure when connected, tendency not to affirm the same peers when not connected and homophily by cohort entry time as in a weekly time frame. CONCLUSIONS: TCs leverage three human tendencies to bring about change. The first is the tendency of cooperators to work together, in this case in seeking graduation. The second is the tendency of people to build clusters. The third is homophily, in this case by cohort entry time. Consistent with TC clinical theory, residents spread affirmations to a variety of peers when they have no previous connection. This suggests that residents balance network clustering with a concern for the community as a whole.

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. This is partly due to available tools not being suited to precisely quantify the role that different organizational principles play in the organization of a subnetwork. Here, we apply a joint modeling technique - the correlation generalized exponential random graph model (cGERGM) - to more completely quantify subnetwork structure. The cGERGM models a correlation network, such as those given in functional connectivity, as a function of activation motifs - consistent patterns of coactivation (i.e., connectivity) between collections of nodes that describe how the regions within a network are organized (e.g., clustering) - and anatomical properties - relationships between the regions that are dictated by anatomy (e.g., Euclidean distance). By jointly modeling all features simultaneously, the cGERGM models the unique variance accounted for by each feature, as well as a point estimate and standard error for each, allowing for significance tests against a random graph and between graphs. Across eight functional subnetworks, we find remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication. Specifically, all subnetworks displayed greater clustering than would be expected by chance, but lower preferential attachment (i.e., hub use). These findings suggest that human functional subnetworks follow a segregated highway structure, in which tightly clustered subcommunities develop their own channels of communication rather than relying on hubs.

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 of 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.

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. Specifically, we report evidence that states are less likely to ratify when states within their region ratify, and results suggesting that countries positively influence other countries at similar levels of economic development. By examining several prominent treaties, we illustrate the complex nature of influence: a single act of ratification can dramatically reshape global environmental politics. More generally, our findings and approach provide an innovative means to understand the evolution and complexity of international environmental protection.

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. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM) - a statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway - suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.

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. However, do indirect connections-such as the ally of an ally or even further out in the alliance network-result in lower probabilities of conflict? We investigate the extent to which military alliances produce peace between states that are not directly allied. We find that the peacemaking horizon of indirect alliances extends through the network up to three degrees of separation. Within this horizon of influence, a lack of decay in the effect of degrees of distance indicates that alliances do not diminish with respect to their ability to affect peace regardless of whether or not the states in question are directly allied. Beyond the three-degree horizon of influence, we observe a sharp decline in the effect of indirect alliances on bilateral peace. Further investigation reveals that the community structure of the alliance network plays a role in establishing this horizon, but the effects of indirect alliances are not spurious to the community structure.

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. Moreover, joint democracy plays little, if any, role in predicting system stability, thus challenging perhaps the major empirical finding of the international relations literature. Lastly, the temporal variability of our measure with conflict is consistent with a causal relationship. Our results have real-world policy implications as changes in our fractionalization measure substantially aid the prediction of conflict up to 10 years into the future, allowing it to serve as an early warning sign of international instability.

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.

The heritability of foreign policy preferences.

Attitudes towards foreign policy have typically been explained by ideological and demographic factors. We approach this study from a different perspective and ex amine the extent to which foreign policy preferences correspond to genetic variation. Using data from the Minnesota Twin Family Study, we show that a moderate share of individual differences in the degree to which one's foreign policy preferences are hawkish or dovish can be attributed to genetic variation. We also show, based on a bivariate twin model, that foreign policy preferences share a common genetic source of variation with political ideology. This result presents the possibility that ideology may be the causal pathway through which genes affect foreign policy preferences.

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. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.