Concept: Social networks
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 3 years ago
Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks, manifesting as a higher tendency of links occurring between people of the same age, race, or political belief. Quantifying the level of assortativity or disassortativity (the preference of linking to nodes with different attributes) can shed light on the organization of complex networks. It is common practice to measure the level of assortativity according to the assortativity coefficient, or modularity in the case of categorical metadata. This global value is the average level of assortativity across the network and may not be a representative statistic when mixing patterns are heterogeneous. For example, a social network spanning the globe may exhibit local differences in mixing patterns as a consequence of differences in cultural norms. Here, we introduce an approach to localize this global measure so that we can describe the assortativity, across multiple scales, at the node level. Consequently, we are able to capture and qualitatively evaluate the distribution of mixing patterns in the network. We find that, for many real-world networks, the distribution of assortativity is skewed, overdispersed, and multimodal. Our method provides a clearer lens through which we can more closely examine mixing patterns in networks.
- The British journal of psychiatry : the journal of mental science
- Published over 3 years ago
BackgroundConnectedness is a central dimension of personal recovery from severe mental illness (SMI). Research reports that people with SMI have lower social capital and poorer-quality social networks compared to the general population.AimsTo identify personal well-being network (PWN) types and explore additional insights from mapping connections to places and activities alongside social ties.MethodWe carried out 150 interviews with individuals with SMI and mapped social ties, places and activities and their impact on well-being. PWN types were developed using social network analysis and hierarchical k-means clustering of this data.ResultsThree PWN types were identified: formal and sparse; family and stable; and diverse and active. Well-being and social capital varied within and among types. Place and activity data indicated important contextual differences within social connections that were not found by mapping social networks alone.ConclusionsPlace locations and meaningful activities are important aspects of people’s social worlds. Mapped alongside social networks, PWNs have important implications for person-centred recovery approaches through providing a broader understanding of individual’s lives and resources.
Social networks play an important role in our daily lives. However, social ties are rather elusive to quantify, especially for large groups of subjects over prolonged periods of time. In this work, we first propose a methodology for extracting social ties from long spatio-temporal data streams, where the subjects are 17,795 undergraduates from a university of China and the data streams are the 9,147,106 time-stamped RFID check-in records left behind by them during one academic year. By several metrics mentioned below, we then analyze the structure of the social network. Our results center around three main observations. First, we characterize the global structure of the network, and we confirm the small-world phenomenon on a global scale. Second, we find that the network shows clear community structure. And we observe that younger students at lower levels tend to form large communities, while students at higher levels mostly form smaller communities. Third, we characterize the assortativity patterns by studying the basic demographic and network properties of users. We observe clear degree assortativity on a global scale. Furthermore, we find a strong effect of grade and school on tie formation preference, but we do not find any strong region homophily. Our research may help us to elucidate the structural characteristics and the preference of the formation of social ties in college students' social network.
Intimate partner violence (IPV) is a growing public health problem, and gaps exist in knowledge with respect to appropriate prevention and treatment strategies. A growing body of research evidence suggests that beyond individual factors (e.g., socio-economic status, psychological processes, substance abuse problems), neighborhood characteristics, such as neighborhood economic disadvantage, high crime rates, high unemployment and social disorder, are associated with increased risk for IPV. However, existing research in this area has focused primarily on risk factors inherent in neighborhoods, and has failed to adequately examine resources within social networks and neighborhoods that may buffer or prevent the occurrence of IPV. This study examines the effects of neighborhood characteristics, such as economic disadvantage and disorder, and individual and neighborhood resources, such as social capital, on IPV among a representative sample of 2412 residents of Toronto, Ontario, Canada. Using a population based sample of 2412 randomly selected Toronto adults with comprehensive neighborhood level data on a broad set of characteristics, we conducted multi-level modeling to examine the effects of individual- and neighborhood-level effects on IPV outcomes. We also examined protective factors through a comprehensive operationalization of the concept of social capital, involving neighborhood collective efficacy, community group participation, social network structure and social support. Findings show that residents who were involved in one or more community groups in the last 12 months and had high perceived neighborhood problems were more likely to have experienced physical IPV. Residents who had high perceived social support and low perceived neighborhood problems were less likely to experience non-physical IPV. These relationships did not differ by neighborhood income or gender. Findings suggest interesting contextual effects of social capital on IPV. Consistent with previous research, higher levels of perceived neighborhood problems can reflect disadvantaged environments that are more challenged in promoting health and regulating disorder, and can create stressors in which IPV is more likely to occur. Such analyses will be helpful to further understanding of the complex, multi-level pathways related to IPV and to inform the development of effective programs and policies with which to address and prevent this serious public health issue.
This study sought to examine whether: (1) the health composition of the social networks of children living in subsidized housing within market rate developments (among higher-income neighbors) differs from the social network composition of children living in public housing developments (among lower-income neighbors); and (2) children’s social network composition is associated with children’s own health. We found no significant differences in the health characteristics of the social networks of children living in these different types of public housing. However, social network composition was significantly associated with several aspects of children’s own health, suggesting the potential importance of social networks for the health of vulnerable populations.
This paper uses concepts from social networks and social exchange theories to describe the implementation of evidence-based practices in afterschool programs. The members of the LEGACY Together Afterschool Project team have been involved in conducting collaborative research to migrate a behavioral strategy that has been documented to reduce disruptive behaviors in classroom settings to a new setting-that of afterschool programs. We adapted the Paxis Institute’s version of the Good Behavior Game to afterschool settings which differ from in-school settings, including more fluid attendance, multiple age groupings, diverse activities that may take place simultaneously, and differences in staff training and experience (Barrish et al. in J Appl Behav Anal 2(2):119-124, 1969; Embry et al. in The Pax Good Behavior Game. Hazelden, Center City, 2003; Hynes et al. in J Child Serv 4(3):4-20, 2009; Kellam et al. in Drug Alcohol Depend 95:S5-S28, 2008; Tingstrom et al. in Behav Modif 30(2):225-253, 2006). This paper presents the experiences of the three adult groups involved in the implementation process who give first-person accounts of implementation: (1) university-based scientist-practitioners, (2) community partners who trained and provided technical assistance/coaching, and (3) an afterschool program administrator. We introduce here the AIMS model used to frame the implementation process conceptualized by this town-gown collaborative team. AIMS builds upon previous work in implementation science using four phases in which the three collaborators have overlapping roles: approach/engagement, implementation, monitoring, and sustainability. Within all four phases principles of Social Exchange Theory and Social Network Theory are highlighted.