Social Networks最新文献

Background

We examined the role of network communication about HIV-related topics in mediating the efficacy of a social network intervention on HIV seroconversion among people who inject drugs (PWID) in Ukraine, where Eastern Europe’s second-largest HIV epidemic is concentrated among PWID.

Methods

We used randomized controlled trial data from 1200 HIV-negative PWID (Ukraine; 2010–2012) in an inverse-odds weighted analysis to examine mediation by network communication.

Results

Network communication mediated 24 % (95 % CI= 19.22–29.38) of the intervention’s effect.

Conclusions

Integrating training to support network communication about additional HIV prevention resources could enhance the impact of social network HIV prevention interventions among PWID.

Social norms influence homophobic behavior, yet these norms are often misperceived. We study the extent to which friendship ties and group memberships are related to misperceptions of opinions towards homosexuality, and how these misperceptions are sustained in social networks through opinion sharing. We find that misperceptions lead individuals to be less willing to share their opinions with ethno-religious ingroup members, non-friends or with individuals whom they perceive to hold different opinions. Although differences observed in the context of this study are relatively small, they may add up over time. These results offer scope for interventions that try to reduce norm misperceptions between groups - as a way to stimulate social change towards a more tolerant society.

Surveys conducted on social groups often generate incomplete information due to imperfect response rates. Drawing on Facebook data from a nationally representative sample of graduating college students in Taiwan, we examined the extent to which partial contact records predict which Facebook users belong to a specific class. We first used data from classes with low to middle response rates to train a model for classmate prediction. Based on data from classes with high or perfect response rates, we simulated data by using four different sampling methods with various response rates, and applied the trained model on simulated data to classmate prediction. With a minimal response rate of 40 percent, we achieved an accuracy rate of 90 percent and a true positive rate of 86 percent. Chronological order sampling had the best prediction performance, followed closely by popularity sampling, then by random sampling, and lastly by unpopularity sampling.

We suggest a novel approach to determining the centrality measures for directed signed networks, based on the notion of social balance. We postulate that along with the existing positive connections, the structure of positive and negative connections can be used to determine potential secondary connections, respectively, weak social ties between pairs of individuals who are, e.g., either friends with the same person or under threat from the same person. This kind of connection agrees perfectly with the theory of social balance. Given the structure of primary and secondary connections, the centrality is measured using an eigenvector-based scheme. The suggested approach is applied to the classical example of the social network of monks in a monastery, and the results show a good agreement with the available ground truth.

A type of symbolic association network for the development of reputation is described and tested. Associations between people in these networks are not based on individual interaction, but rather are created by “reputational entrepreneurs” based on perceived symbolic association between people. We argue the intent of this type of connection is to add to the reputational information about those connected and we test whether a network of such associations influence cultural recognition. To do this, we use dyadic connections between classical music composers created by conductors for orchestra performance and determine whether a composer’s symbolic association network (SAN) aids recognition in publications. We find SANs to have a significant impact on the extent of reputational recognition, even when holding a composer’s individual status achievements constant. Composers with a large symbolic association network and those who bridge unconnected composers tend to receive more recognition. We discuss the influence of symbolic association networks on perception of reputational significance. We suggest SANs may advance research in reputation and culture particularly when considering actors whose reputation is active beyond their work or lifetime, such as artists, writers, musicians, and historical figures.

The paper builds on the framework proposed by Pattison and Snijders (2012) for specifying exponential random graph models (ERGMs) for social networks. We briefly review the two-dimensional hierarchy of potential dependence structures for network tie variables that they outlined and provide proofs of the relationships among the model forms and of the nature of their sufficient statistics, noting that models in the hierarchy have the potential to reflect the outcome of processes of cohesion, closure, boundary and bridge formation and path creation over short or longer network distances. We then focus on the so-called partial inclusion dependence assumptions among network tie variables and the pendant-triangle, or paw, statistics to which they give rise, and illustrate their application in an empirical setting. We argue that the partial inclusion assumption leads to models that can reflect processes of boundary and bridge formation and that the model hierarchy provides a broad and useful framework for the statistical analysis of network data. We demonstrate in the chosen setting that pendant-triangle (or paw) effects, in particular, lead to a marked improvement in goodness-of-fit and hence add a potentially valuable capacity for modelling social networks.

The sociology of elites has long considered families as the unit of analysis in studies of power dynamics between elite dynasties and their transmission of wealth and prestige over generations. However, the assumption that families are cohesive units with common goals and agendas does not hold, especially for large and powerful family dynasties. Internal conflicts and clan rivalries throughout history suggest that independent clans, rather than families, are the more appropriate level for aggregation. The increasing availability of large-scale genealogical datasets and advances in social network analysis allow this more fine-grained perspective to be implemented even without historical documentation on observed clan structures. This paper builds on socio-anthropological conceptualizations of kinship and on hierarchical clustering techniques to present a new method for identifying independent clans within families that relies only on network-dependent terms. I use simulated data and an empirical kinship network of families of early modern Basel, Switzerland to compare a clan detector algorithm’s performance with common community detection techniques. The historical accuracy of the clan structures detected is further assessed with various status indicators. The analyses show that the proposed clan detector algorithm is more suitable for identifying historically accurate clans than the traditional approaches. The application of the new method to the kinship network of Basel families sheds light on the city’s stratification into high- and low-status societies in which elite families were also divided into privileged and less privileged clans.

Spending concentration, political influence, and collusion violate rules and principles of open and fair public procurement, leading to corrupt contract allocation. This study adopts stochastic actor-oriented models to test the evolution of these forms of procurement corruption risks in a longitudinal network study of 33579 construction contracts pertaining to Guatemalan local governments from 2012 to 2020. We identify a range of network configurations, based on past empirical research and theory, that capture different patterns of suspicious micro tendencies suggestive of corruption. We show how these micro tendencies shift in strength according to changes in electoral cycles and anti-corruption interventions, thus shedding light on how interventions may temporarily impact corrupt behavior, and how it may adapt and persist after a period of transition. The results indicate that collusion and spending concentration play significant roles in sustaining the risk of corrupt contract allocation, and that this behavior is able to rebound even after the introduction of anti-corruption interventions and new political regimes. The findings underscore the importance of local interventions and advocate for network approaches to enhance transparency, accountability, and long-term anti-corruption efforts.

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.