Social Networks最新文献

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.

Though network scholars long have observed that people experience high rates of network turnover, theorization on how ties dissolve has lagged behind, with natural tie decay dominating as the primary explanation. Many deviants face stigma and become social pariahs from communities of origin, yet current network theories struggle to explain how such processes of network dissolution occur. Using interviews conducted with Korean unwed mothers, a highly stigmatized and hard-to-reach population, this study shows that shared culture in networks—norms, expectations, and meanings—plays a key role in how people navigate stigma in their networks. I demonstrate that tie severance requires work: relational work that matches network-specific culture. I propose a typology of tie severance processes along two dimensions, actor and motive, and identify associated relational work for each type. This study shows that macro-level cultural values are adapted to each network and (re)interpreted before relational work can be engaged to manage social relations.

This article investigates how variation in the social positions (occupations) presented by a position generator (PG) instrument affects the reliability of egocentric network measures based on PG data. We modify the split-half design employed in Verhaeghe et al.’s (2013) study of university students for use with already-existing PG data on a national adult population. After replicating that study, we examine how reliability varies with the relational criterion (e.g., friendship) that links an individual to an occupation and with the number of occupations in a PG. We find that most PG measures are only modestly reliable (i.e., are relatively sensitive to occupational selection), but our absolute assessment of their reliability (given instrument length) is somewhat more optimistic than that of the prior study. Extensity (the number of positions with which a subject has contact) is the most reliable measure, composition measures based on social class groupings are next, and those that involve socioeconomic standing or prestige scores are least reliable. Deeming someone to be connected to an occupation using an acquaintance criterion yields more reliable measures than requiring a stronger level of connectivity. PG measures based on longer (i.e., more occupations) instruments have higher reliability, and projections for longer PGs suggest that including 20 occupations could measure extensity and counts of contacts in some class groupings with adequate reliability; but other class composition measures and all measures involving socioeconomic standing or prestige scores would require 30 or more.

Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed events grows. This paper addresses this issue by introducing the Deep Relational Event Additive Model (DREAM) as a solution to the computational challenges presented by modeling non-linear effects in Relational Event Models (REMs). DREAM relies on Neural Additive Models to model non-linear effects, allowing each effect to be captured by an independent neural network. By strategically trading computational complexity for improved memory management and leveraging the computational capabilities of graphic processor units (GPUs), DREAM efficiently captures complex non-linear relationships within data. This approach demonstrates the capability of DREAM in modeling dynamic networks and scaling to larger networks. Comparisons with traditional REM approaches showcase DREAM superior computational efficiency. The model potential is further demonstrated by an examination of the patent citation network, which contains nearly 8 million nodes and 100 million events.

Group work in classrooms is employed by teachers across all levels of education. For group work to be effective, all students should participate equally. Why some students engage in interaction and how group size and composition influence interaction dynamics is a research gap. We employed dynamic actor-oriented models on a sample of 145 Czech lower-secondary students in 62 small groups and pooled the results from the groups with a meta-analytical procedure. We found bursty behavior resulting from endogenous structural mechanisms of reciprocity, transitivity, cyclicity, and preferential attachment. Students gave preference to initiating interactions with those they initiated interactions with before and off-task interaction contributed to the development of on-task interaction. Students strongly preferred interactions with friends. Those students who talked a lot during regular whole-classroom lessons and students with high levels of literacy tended to both initiate and receive more interactions in group work, and students similar in these attributes preferred to interact with each other. Group size did not affect preferential attachment tendencies in interaction, but smaller groups made the effect of friendship ties on interactions stronger, and communication group norms shifted with changing group composition. Our study shows the suitability of dynamic actor-oriented models for studying interaction in education and small groups.

This study expands upon prior analysis of the case citation network of the International Court of Justice (ICJ) by demonstrating that reverse page-rank outperforms hub and authority score in identifying significant sources of precedent. Application of reverse page-rank reveals that though ICJ decisions that are well grounded in prior cases tend to become more important sources of precedent, this relationship is weak when compared to the US Supreme Court. This tempers past conclusions that the ICJ has become more similar to common law systems but supports a view of the Court as an institution that balances consistency in its application of international law with the statutory limits on using its decisions as sources of such law. In addition, a novel method for constructing agreement networks between judges is leveraged to identify ICJ judges with influential positions. This provides evidence that certain judges may be more indicative of which decisions become important in the future. Though the strength of this influence is also limited, it nevertheless suggests that some ICJ judges may better represent the development of international legal precedent than others.