jimi adams

Sex, Drugs, and Race: How Behaviors Differentially Contribute to the Sexually Transmitted Infection Risk Network Structure

OBJECTIVES We examined how risk behaviors differentially connect a population at high risk for sexually transmitted infections. METHODS Starting from observed networks representing the full risk network and the risk network among respondents only, we constructed a series of edge-deleted counterfactual networks that selectively remove sex ties, drug ties, and ties involving both sex and drugs and a comparison random set. With these edge-deleted networks, we have demonstrated how each tie type differentially contributes to the connectivity of the observed networks on a series of standard network connectivity measures (component and bicomponent size, distance, and transitivity ratio) and the observed network racial segregation. RESULTS Sex ties are unique from the other tie types in the network, providing wider reach in the network in relatively nonredundant ways. In this population, sex ties are more likely to bridge races than are other tie types. CONCLUSIONS Interventions derived from only 1 mode of transmission at a time (e.g., condom promotion or needle exchange) would have different potential for curtailing sexually transmitted infection spread through the population than would attempts that simultaneously address all risk-relevant behaviors.

Hoop inequalities: Race, class and family structure background and the odds of playing in the National Basketball Association

The popular image of the African American National Basketball Association (NBA) player as rising from the ‘ghetto’ to international fame and fortune misleads academics and publics alike. This false image is fueled, in part, by critical shortcomings in empirical research on the relationship between race, sport, and occupational mobility: these studies have not adequately examined differences in social class and family structure backgrounds across, and especially within, racial groups. To address this problem, we empirically investigate how the intersection of race, social class and family structure background influences entry into the NBA. Information on social class and family structure background for a subpopulation of NBA players (N = 155) comes from 245 articles published in local, regional and national newspapers between 1994 and 2004. We find that, after accounting for methodological problems common in newspaper data, most NBA players come from relatively advantaged social origins and African Americans from disadvantaged social origins have lower odds of being in the NBA than African American and white players from relatively advantaged origins. A discussion of the implications of these findings for academics and publics concludes the article.

Mapping interdisciplinary fields: efficiencies, gaps and redundancies in HIV/AIDS research.

While interdisciplinarity continues to increase in popularity among funders and other scientific organizations, its potential to promote scientific advances remains under-examined. For HIV/AIDS research, we examine the dynamics of disciplinary integration (or lack thereof) providing insight into a fields knowledge base and those questions that remain unresolved. Drawing on the complete histories of two interdisciplinary journals, we construct bibliographic coupling networks based on overlapping citations to identify segregation into research clusters and estimate topic models of research content. We then compare how readily those bibliographic coupling clusters account for the structuring of topics covered within the field as it evolves over two decades. These comparisons challenge one-dimensional and/or cross-sectional approaches to interdisciplinarity. Some topics are increasingly coordinated across disciplinary boundaries (e.g., vaccine development); others remain relatively segmented into disconnected disciplinary domains for the full period (e.g., drug resistance). This divergence indicates heterogeneity in interdisciplinarity and emphasizes the need for critical approaches to studying the organization of science.

Knowledge in motion: the evolution of HIV/AIDS research

Many contemporary social and public health problems do not fit neatly into the research fields typically found in universities. With this in mind, researchers and funding agencies have devoted increasing attention to projects that span multiple disciplines. However, comparatively little attention has been paid to how these projects evolve over time. This relative neglect is in part attributable to a lack of theory on the dynamic nature of such projects. In this paper, we describe how research programs can move through various states of integration including disciplinarity, multidisciplinarity, interdisciplinarity and transdisciplinarity. We link this insight to computational techniques—topic models—to explore one of the most vibrant and pressing contemporary research areas—research on HIV/AIDS. Topic models of over 9000 abstracts from two prominent journals illustrate how research on HIV/AIDS has evolved from a high to a lower level of integration. The topic models motivate a more detailed historical analysis of HIV/AIDS research and, together, they highlight the dynamic nature of knowledge production. We conclude by discussing the role of computational social science in dynamic models of interdisciplinarity.

How Initial Prevalence Moderates Network-based Smoking Change: Estimating Contextual Effects with Stochastic Actor-based Models.

We use an empirically grounded simulation model to examine how initial smoking prevalence moderates the effectiveness of potential interventions designed to change adolescent smoking behavior. Our model investigates the differences that result when manipulating peer influence and smoker popularity as intervention levers. We demonstrate how a simulation-based approach allows us to estimate outcomes that arise (1) when intervention effects could plausibly alter peer influence and/or smoker popularity effects and (2) across a sample of schools that match the range of initial conditions of smoking prevalence in U.S. schools. We show how these different initial conditions combined with the exact same intervention effects can produce substantially different outcomes—for example, effects that produce smoking declines in some settings can actually increase smoking in others. We explore the form and magnitude of these differences. Our model also provides a template to evaluate the potential effects of alternative intervention scenarios.

Quantifying the Benefits of Link-Tracing Designs for Partnership Network Studies.

Difficult-to-reach populations are frequently sampled through various link tracing-based designs, which rely on interpersonal networks to identify members of the population. This article examines the substantive returns to one such multiple-link tracing design in the Colorado Springs “project 90” human immunodeficiency virus (HIV) risk networks study. Cross-links were respondents who were targeted for enrollment because of being named as partners by at least two other respondents in the sample. The authors compare cross-links to other respondents on sociodemographic characteristics and network properties using bivariate and multivariate adjusted statistics. The authors evaluate their contributions to observed network structure by creating a set of counterfactual networks deleting the information they provided. Results suggest that the link-tracing techniques led to identifying populations that would have otherwise been missed and that their absence would have underestimated potential HIV risk by distorting epidemiologically relevant measures within the network.

Glee's McKinley High: Following Middle America's sexual taboos

Writers for popular media frequently draw on insights known about social networks in developing their plotlines and character biographies (whether in books, television, movies, etc.). Perhaps most known to network analysts in this respect, Freeman (2000) presents a collection of network concepts represented in comic strips. These depictions often are consistent with the patterns network analysts observe in real-world empirical examples. For example, the long-running sitcom Friends exhibited strong homophily (McPherson et al., 2001) or assortative mixing on race and socioeconomic status among the main characters. Other times the violation of these typical patterns can serve to generate dramatic tension or a source of comedy. For example transitivity—or the tendency of ones friends to also become friends (Holland & Leinhardt, 1972)—is absent in the movie Hush where Jessica Langes character plots to kill the daughter-in-law she does not like. P-O-X social balance (Heider, 1948) describes the tendency for friends to share common interests, which was violated to comedic effect in the Seinfeld episode where Jerrys character simply cannot accept his dates refusal to try a taste of the pie he finds delicious, bothering him for days and ultimately leading to his ending the relationship.

The coevolution of networks and health: Introduction to the Special Issue of Network Science.

Historically, health has played an important role in network research, and vice versa (Valente, 2010). This intersection has contributed to how we understand human health as well as the development of network concepts, theory, and methods. Throughout, dynamics have featured prominently. Even when limited to static methods, the emphasis in each of these fields on providing causal explanations has led researchers to draw upon theories that are dynamic, often explicitly. Here, we elaborate a variety of ways to conceptualize the relationship between health and network dynamics, show how these possibilities are reflected in the existing literature, highlight how the articles within this special issue expand that understanding, and finally, identify paths for future research to push this intersection forward.

Epidemic potential by sexual activity distributions.

For sexually transmitted infections like HIV to propagate through a population, there must be a path linking susceptible cases to currently infectious cases. The existence of such paths depends in part on the degree distribution. Here, we use simulation methods to examine how two features of the degree distribution affect network connectivity: Mean degree captures a volume dimension, while the skewness of the upper tail captures a shape dimension. We find a clear interaction between shape and volume: When mean degree is low, connectivity is greater for long-tailed distributions, but at higher mean degree, connectivity is greater in short-tailed distributions. The phase transition to a giant component and giant bicomponent emerges as a positive function of volume, but it rises more sharply and ultimately reaches more people in short-tail distributions than in long-tail distributions. These findings suggest that any interventions should be attuned to how practices affect both the volume and shape of the degree distribution, noting potential unanticipated effects. For example, policies that primarily affect high-volume nodes may not be effective if they simply redistribute volume among lower degree actors, which appears to exacerbate underlying network connectivity.