Social Networks最新文献

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.

This study explores variation in composition and strength of close friendship ties across timing and contexts of tie formation. Analyzing South Korean survey data and comparing it with existing U.S. and Korean network data, we find both similarities and differences between friendship and non-kin discussion networks in the two countries. We show that schools are a crucial source of close friendships for Koreans, shaping homophily patterns. Additionally, we uncover substantial variations in tie strength across timings and contexts of tie formation. We discuss the implications of these findings, highlighting the role of social and cultural contexts in shaping personal networks.

This study contributes to the social connectedness literature by exploring the range of social interactions that people experience on a daily basis using time diary data. First, we investigate the different types of people whom individuals encounter in everyday life (i.e., family, friends, co-workers, acquaintances, others). Quantifying the degree of this social exposure provides insight into potential sources of support, information, comparison, and control. It further allows us to capture certain types of social connections that are commonly missed in traditional social network questionnaires. Second, we examine how social context fosters interactions between different types of people. Upon analyzing data from the American Time Use Survey, we find that a majority of respondents (69%) engaged in at least one interaction with a non-household member in a 24-hour period. We also found that interactions with different types of people varied according to social context. Interactions with friends, acquaintances, and “others” were significantly more likely to occur in public contexts rather than private or professional contexts. Collectively, our findings highlight the prevalence of social exposure among the American population as well as the important role of social context in making these interactions possible.

Although negative ties may cause stress and harm well-being, they are also considered fundamental in close and ongoing relationships. This study distinguishes positive, negative, and – when characterized by both valences – ambivalent ties. Analyzing almost 10,000 personal networks from the Swiss CH-X study shows that: (1) ambivalence among family members is more prevalent than among non-family members, (2) ambivalent family dyads or triads are not negatively associated with well-being, and (3) certain balanced family triads are associated with higher well-being and an unbalanced non-family triad is associated with lower well-being. These results suggest that conflicts are not necessarily detrimental to young adults’ well-being.

Calculating the true probability that a signal will be transmitted between any pair of nodes in a network is computationally hard. Diffusion centrality, which counts the expected number of times that a signal will be transmitted, is often used as a heuristic for this probability. But this formula can lead to distorted results when used in this way, because its summation of probabilities does not take account of the inclusion–exclusion principle. This paper provides a simple new formula for the probabilities of node-to-node diffusion in networks, which uses De Morgan’s laws to account for the inclusion–exclusion principle. Like diffusion centrality, this formula is based on the assumption that the probabilities of a signal travelling along each walk in a network are independent. The probabilities it calculates are therefore called Walk-Independence Probabilities (WIP). These probabilities provide two new centrality measures, WIP centrality and blocking centrality. Blocking centrality is a type of induced centrality which is calculated when some nodes block signals.

This study examined the impact of different social networks on the mental health outcomes of older adults during the COVID-19 pandemic. Participants were 25,534 older adults from the Survey of Health, Ageing and Retirement in Europe (SHARE). The study identified five social network profiles (family, friends, spouse, diverse, others) and a “no network” group. Findings showed that, compared to the no network profile, those with spouse and family profiles are more protected against depression and loneliness during the COVID-19. In turn, individuals within friends and diverse profiles had a similar likelihood of feeling depressed, anxious, and lonely. Friends and diverse profiles had higher likelihood of suffering more anxious or lonelier than before the COVID-19 compared to the no network profile. The study further discusses possible explanations about why these profiles, which typically served as mental health protectors, were significantly affected by the unique circumstances of the COVID-19 pandemic.