Social Networks最新文献

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.

We propose a social network-aware approach to study socio-economic segregation. The key question that we address is whether patterns of segregation are more pronounced in social networks than in the common spatial neighborhood-focused manifestations of segregation. We, therefore, conduct a population-scale social network analysis to study socio-economic segregation at a comprehensive and highly granular social network level. For this, we utilize social network data from Statistics Netherlands on 17.2 million registered residents of the Netherlands that are connected through around 1.3 billion ties distributed over five distinct tie types. We take income assortativity as a measure of socio-economic segregation, compare a social network and spatial neighborhood approach, and find that the social network structure exhibits two times as much segregation. As such, this work complements the spatial perspective on segregation in both literature and policymaking. While at a widely used unit of spatial aggregation (e.g., the geographical neighborhood), patterns of socio-economic segregation may appear relatively minimal, they may in fact persist in the underlying social network structure. Furthermore, we discover higher social network segregation in larger cities, shedding a different light on the common view of cities as hubs for diverse socio-economic mixing. A population-scale social network perspective hence offers a way to uncover hitherto “hidden” segregation that extends beyond spatial neighborhoods and infiltrates multiple aspects of human life.

In this study, we present a method to uncover the fundamental dimensions of structural variability in Personal Networks (PNs) and develop a classification solely based on these structural properties. We address the limitations of previous literature and lay the foundation for a rigorous methodology to construct a Structural Typology of PNs. We test our method with a dataset of nearly 8,000 PNs belonging to high school students. We find that the structural variability of these PNs can be described in terms of six basic dimensions encompassing community and cohesive subgroup structure, as well as levels of cohesion, hierarchy, and centralization. Our method allows us to categorize these PNs into eight types and to interpret them structurally. We assess the robustness and generality of our methodology by comparing with previous results on structural typologies. To encourage its adoption, its improvement by others, and to support future research, we provide a publicly available Python class, enabling researchers to utilize our method and test the universality of our results.

Bridge supervision occurs when manager and boss operate in separate social worlds, a condition increasingly likely as managers work more often from locations outside the office. The concept of bridge supervision was proposed using evidence from managers balkanized into product and geographic silos. Silos facilitate managers segregated from the boss. We here try to test support for bridge supervision hypotheses in a cohesive population of HR managers, where bridge supervision would be more difficult. Cohesion does limit the evidence of bridge supervision, but we nevertheless confirm the phenomenon's central hypotheses: bridge supervision is associated with role segregation between manager and boss, and manager performance is unaffected. More, by explicitly considering a broader set of network indicators of bridge supervision, we replicate Burt and Wang’s (2022) focus on just two: mutual contacts, and manager-exclusive density. We close with key features of bridge supervision now replicated, cautions on how easily the phenomenon can be undetected in a cohesive study population, and implications for future research.

The culmination of network and organization research suggests diverse network ties bolster innovation. Extending this line of research, this study examines how three distinct dimensions of network portfolio diversity—duration, tie strength, and collaboration type—are related to organizational innovation. Survey, ego network, and expert evaluation data on 258 U.S. social ventures suggest that these different dimensions have differential impact on the novelty of ideas organizations develop to tackle social issues (i.e., social innovation). Specifically, while diversity in relationship duration with nonprofit partners had an inverted U-shaped curvilinear relationship with social innovation, diversity in collaboration type with business partners and diversity in tie strength with government partners each had a U-shaped curvilinear relationship. These results highlight that the relationship between network diversity and organizational innovation is contingent on the network portfolio dimension, as well as the partner sector. This research contributes to theorizing on social and interorganizational networks, cross-sector partnerships, and social innovation. Findings also have implications for organizational leaders as they configure heterogeneous ego network portfolios to boost innovation for social impact.

This study examines the adoption of a new preventive treatment for HIV called preexposure prophylaxis (PrEP) in a nation-wide network of US physicians. We compare the structure of these networks across nine multi-state census regions, and assess geographic variations in network structure. Within these networks, we measured the adoption threshold associated with physician adoption of PrEP. The low threshold values that we observe are consistent with the hypothesis that slow PrEP adoption is the result of a lack of knowledge and exposure among physicians. Regression results demonstrate the mix of market, epidemiological, and socio/cultural factors that shape adoption thresholds.