Social Networks最新文献

Despite a growing recognition of the importance of insurgent group structure on conflict outcomes, there is very little empirical research thereon. Though this problem is rooted in the inaccessibility of data on militant group structure, insurgents frequently publish large volumes of image data on the internet. In this paper, I develop a new methodology that leverages this abundant but underutilized source of data by automating the creation of a social network graph based on co-appearance in photographs using deep learning. Using a trove of 20,645 obituary images published online by the PKK, a Kurdish militant group in Turkey, I demonstrate that an individual’s centrality in the resulting co-appearance network is closely correlated with their rank in the insurgent group.

While previous research indicates that students benefit from their peers’ resources, little is known about access to social capital in the school context. Therefore, this study examines differential access to social capital – measured by friends’ socioeconomic status (SES), the number of books they have at home, and their reading habits – in secondary schools in Germany, the Netherlands, and Sweden. Relying on a large-scale dataset, I investigate the association between socioeconomic status, minority status, and social capital using complete friendship network information. I argue that social capital access is connected to a two-stage process consisting of school sorting and friendship selection. To differentiate between these two processes, I apply within-between random effects (REWB). The models show that friendship selection is much less relevant for access to social capital than school sorting. Results indicate that while high-SES students have better access to social capital across dimensions, access patterns for minority students are more nuanced.

Despite the progress in pharmaceutical and epidemiological tools for combating HIV spread, HIV stigma remains a significant social barrier impeding treatment and prevention efforts, potentially reducing the effectiveness of interventions to reduce HIV transmission. In this paper, we propose a novel approach to defining and estimating HIV stigmatization through the structure of sexual relations, as opposed to attitudes. We conceptualize structural stigma as arising from two mechanisms: (1) a reduced propensity towards HIV serodiscordant partnerships (exclusion); and (2) a reduced propensity towards partnerships with seroconcordant individuals who themselves have serodiscordant partnerships (ostracism). Both mechanisms can be assessed from observed partnership network data using exponential family random graph models (ERGMs). We demonstrate our approach on a sexual contact network of black men who have sex with men in the South Side of Chicago. We find a tendency for serodiscordant sexual relationships to be suppressed in the network ($\theta$ = −0.69, p $<$ .05), as well as a suppressive tendency for HIV negative YBMSM to have sex with other HIV negative people in serodiscordant relationships ($\theta$ = −0.96, p $<$ .05) suggesting that structural HIV stigma is present in this network. Potential relationships with attitudinal stigma and implications for epidemiological strategies for reducing HIV stigma are discussed.

Researchers often use pooled exponential random graph models (ERGM) to analyze samples of networks. However, pooled ERGM—here, understood to include both meta-regression and combined estimation on a stacked adjacency matrix—may be biased if there is heterogeneity in the latent error variance (‘scaling’) of each lower-level model. This study explores the implications of scaling for pooled ERGM analysis. We illustrate that scaling can produce bias in pooled ERGM coefficients that is more severe than in single-network ERGM and we introduce two methods for reducing this bias. Simulations suggest that scaling bias can be large enough to alter conclusions about pooled ERGM coefficient size, significance, and direction, but can be substantially reduced by estimating the marginal effect within a block diagonal or random effects meta-regression framework. We illustrate each method in an empirical example using Add Health data on 15 in-school friendship networks. Results from the application illustrate that many substantive conclusions vary depending on choice of pooling method and interpretational quantity.

In this study, we explore the relationship between inter-organizational network dynamics and innovation outcomes. We focus on node turnover and argue that both cluster and broker dynamics can range from low (stable) to high (volatile), resulting in differentiated outcomes. The data comprises 318 consortium members participating in 104 R&D consortia forged in a 23-year period in the Dutch water sector. Our analysis reveals two equifinal combinations (stable brokers – volatile clusters and volatile brokers – stable clusters) that both generate significantly higher innovation outcomes compared to networks with low, moderate, or high dynamics across the entire network.

Communal coping may benefit caregivers, but most communal coping research focuses on dyads. Using an egocentric network design, we examine caregivers’ we-talk—a linguistic marker of shared responsibility—and caregiver reports of 1) network member involvement in collaborative care roles and 2) met/unmet expectations across typically developing and rare disease contexts. We-talk was linked to involvement in direct care and support, but links of we-talk to decision-making varied based on network member closeness; we-talk was linked to meeting expectations for decision-making only. There were no differences across context, suggesting shared responsibility is linked to collaborative roles across caregiving contexts.

Socioeconomic homophily in friendship networks is the result of several co-occurring processes, which are extremely challenging to disentangle. We propose to study the particular context of a three-week summer camp in France that gathered teenagers from varied socioeconomic backgrounds. We argue that this camp provides a unique opportunity to observe the sociability of adolescents in well-bounded settings with equalized meeting opportunities, thus helping us to narrow down the specific role of homophilic selection - the process by which individuals actively select similar friends. We use Stochastic Actor-Oriented Models to analyze the declared friendships of participants. Furthermore, we introduce a novel longitudinal extension of the Exponential Random Partition Model (ERPM), a model developed for relational data organized in discrete groups, which we apply to observed meal-sharing among adolescents. Results point to weak to non-existent homophilic selection during the stay. In turn, this suggests that structural opportunities and contextual moderators might be essential in explaining the amount of socioeconomic homophily found at school or in other contexts.

We provide a characterization of closeness centrality in the class of distance-based centralities. To this end, we introduce a natural property, called majority comparison, that states that out of two adjacent nodes the one closer to more nodes is more central. We prove that any distance-based centrality that satisfies this property gives the same ranking in every graph as closeness centrality. The axiom is inspired by the interpretation of the graph as an election in which nodes are both voters and candidates and their preferences are determined by the distances to the other nodes.

This article discusses some network mechanisms that may facilitate involvement in artistic production. More specifically, it explores the nature of the collaborative networks in which musicians are embedded, and looks for the structural configurations that are most conducive to individual creativity. Drawing upon a dataset consisting of 253 music teachers in the Italian region of Trentino, and focusing in particular on record production, the article shows productivity and artistic vitality to be highest among musicians with a balanced combination of ties to other musicians in the local music schools system, and ties to musicians with different territorial locations. These findings contribute to established lines of research on the network determinants of individual productivity and creativity in various domains.

In recent years there has been an increasing interest in the use of relational event models for dynamic social network analysis. The basis of these models is the concept of an “event”, defined as a triplet of time, sender, and receiver of some social interaction. The key question that relational event models aim to answer is what drives the pattern of social interactions among actors. Researchers often consider a very large number of predictors in their studies (including exogenous effects, endogenous network effects, and interaction effects). However, employing an excessive number of effects may lead to overfitting and inflated Type-I error rates. Moreover, the fitted model can easily become overly complex and the implied social interaction behavior difficult to interpret. A potential solution to this problem is to apply Bayesian regularization using shrinkage priors to recognize which effects are truly nonzero (the “wheat”) and which effects can be considered as (largely) irrelevant (the “chaff”). In this paper, we propose Bayesian regularization methods for relational event models using four different priors for both an actor and a dyad relational event model: a flat prior model with no shrinkage, a ridge estimator with a normal prior, a Bayesian lasso with a Laplace prior, and a horseshoe prior. We apply these regularization methods in three different empirical applications. The results reveal that Bayesian regularization can be used to separate the wheat from the chaff in models with a large number of effects by yielding considerably fewer significant effects, resulting in a more parsimonious description of the social interaction behavior between actors in dynamic social networks, without sacrificing predictive performance.