Social Networks最新文献

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.

While the economic benefits of internal migration are widely documented, the social costs of internal migration have received comparatively less attention. In addition, most studies focus on the impact of the last-recorded migration, ignoring the cumulative impact of successive migrations. Grounded in the life-course trajectory approach to migration and the convoy model of social networks, this paper addresses this gap by applying sequence and cluster analysis to retrospective data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 26 European countries to establish internal migration trajectories based on the timing, frequency, and direction of migration between NUTS-2 regions. The results reveal that differences in social networks between lifetime stayers, childhood migrants and one-time adult migrants are minimal. A more complex picture emerges for repeat migrants who account for half migrants and are split between return migrants, serial onward migrants, and circular migrants. Regression results show that repeat migrants – whether onward, return, or circular – display social networks less focused on family and more geographically dispersed, which results in a lower frequency of contact than lifetime stayers. However, repeat migrants report the same level of overall satisfaction with their social networks as lifetime stayers, which suggests that they start with different expectations than stayers or simply adjust their expectations in response to the social costs and benefits of migration.

For different currents in policy analysis as policy networks and the Advocacy Coalition Framework (ACF), identifying coalitions from policy beliefs and coordination between actors is crucial to a precise understanding of a policy process. Focusing particularly the relational dimension of ACF approaches linked with policy network analysis, determining policy subsystems from the actor collaborations and exchanges has recently begun offering fertile links with the network analysis. Studies in this way frequently apply Block Modeling and Community Detection (BMCD) strategies to define homogeneous political groups. However, the BMCD literature is growing quickly, using a wide variety of algorithms and interesting selection methods that are much more diverse than those used in the policy network analysis and particularly the ACF when this current focused on the collaboration networks before or after regarding the belief distance between actors. Identifying the best methodological option in a specific context can therefore be difficult and few ACF studies give an explicit justification. On the other hand, few BMCD publications offer a systematic comparison of real social networks and they are never applied to policy network datasets. This paper offers a new, relevant 5-Step selection method to reconcile advances in both the policy networks/ACF and BMCD. Using an application based on original African policy network data collected in Madagascar and Niger, we provide a useful set of practical recommendations for future ACF studies using policy network analysis: (i) the density and size of the policy network affect the identification process, (ii) the “best algorithm” can be rigorously determined by maximizing a novel indicator based on convergence and homogeneity between algorithm results, (iii) researchers need to be careful with missing data: they affect the results and imputation does not solve the problem.

Past research indicates interviewer effects lead to an underestimation of network size and higher nonresponse to the “important matters” name generator. Self-administered surveys offer a potential solution, but evidence is mixed and context-specific. We employ a logistic multilevel regression, estimated using a Bayesian Markov Chain Monte Carlo approach, to analyze nonresponse to this name generator from 33 post-electoral surveys across 21 countries in the Comparative National Election Project. We find higher nonresponse in interviewer-administered surveys compared to self-administered surveys, particularly among specific demographic groups. Finally, we discuss the trade-offs in selecting survey modes for collecting ego-network data using this instrument.

This research examines whether categorical closure – an increased tendency for closure in homogeneous triads – matters for tie formation and tie persistence. We utilized 2019–2020 panel data on students’ networks at three law schools and employed separable temporal exponential random graph models to examine whether closed triads with shared identities were more likely to form and to persist over time. We also investigated whether closed triads based on shared organizational assignments were associated with lower likelihoods of tie formation and tie persistence over time. Results supported the notion that law students were more likely to form homogeneous closed triads based on shared categories, particularly family background, gender, and race, while closed triads based on organizational assignments were less likely. Closed triads tended to persist over time, but there was some support for the notion that homogeneous closed triads based on family background, college rank, and sexuality were more durable. This study highlights categorical closure as an additional network mechanism giving rise to homogenous groups.

Coming into relations involves exiting a state of indecision and deciding whether to relate or not. Little research has focused on these initial moments, the communications involved, and the making of a relational decision. We study this process using 947 speed dating encounters, their minute-by-minute communications, and the reported timing of relational decisions. We show that certain forms of communication reveal an actor’s relational state of being undecided, desiring a relation, or not desiring a relation (revealing signals). For example, indecision corresponds with indirect and ambiguous communication (negative facework); desiring a relation entails positive, excited, and entraining communication (positive facework); and not desiring a relation involves routine talk. We also show that certain forms of communication persuade persons to transition relational states, moving beyond their indecision and coming to a relational decision (persuasive signals). Interestingly, only some revealing signals are persuasive and bring about corresponding relational decisions in others. These tend to be clear signals that cannot be attributed to the situation or politeness. Last, some signals persuade relational decisions without corresponding to a relational state. These performative signals are select forms of ambiguous communication that place the speaker in an advantaged position within social exchange.