Social Networks最新文献

Statistical analysis of bipartite networks frequently requires randomly sampling from the set of all bipartite networks with the same degree sequence as an observed network. Trade algorithms offer an efficient way to generate samples of bipartite networks by incrementally ‘trading’ the positions of some of their edges. However, it is difficult to know how many such trades are required to ensure that the sample is random. I propose a stopping rule that focuses on the distance between sampled networks and the observed network, and stops performing trades when this distribution stabilizes. Analyses demonstrate that, for over 650 different degree sequences, using this stopping rule ensures a random sample with a high probability, and that it is practical for use in empirical applications.

The present empirical study aims to explore medical knowledge sharing in the Australian healthcare context, aiming to broadly evaluate the potential impact of Project ECHO®, an online mentoring and networking health program. We focus on health-related knowledge sharing practices among the network of professionals through formal and informal channels, and across different health and non-health sectors and organisational systems. Studying knowledge transmission among professional networks is essential for optimizing healthcare delivery, promoting innovation, and providing insights on improvement of patient experiences within the healthcare system. We utilize a multilevel approach to shape our data collection strategy. Employing network measures and Multilevel Exponential Random Graph Models, we aim to explore how advice and knowledge sharing behaviours among healthcare professionals and their institutions are interdependently connected. Then, we incorporate network generated results within an evaluation framework for establishing some aspects of the efficiency of the ECHO program along four pillars: Acceptability, Capability, Reachability, and Integration. Our investigation found that among ECHO members, hierarchy is less pronounced compared to across levels and organizations, with certain individuals emerging as central in advice-sharing. The multilevel network perspective showed complex, informal patterns of knowledge and information sharing, including inter-organizational hierarchy, role and sector homophily, brokerage roles with popularity across health organizations, and connectivity through knowledge-sharing in cross-level small group clusters.

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.