Obesity-related behaviors in adolescent friendship networks

This study examines obesity-related behaviors within adolescent friendship networks, because adolescent peers have been identified as being important determinants of many health behaviors. We applied ERGM selection models for single network observations to determine if close adolescent friends engage in similar behaviors and to explore associations between behavior and popularity. Same-sex friends were found to be similar on measures of organized physical activity in two out of three school-based friendship networks. Female friends were found to engage in similar screen-based behaviors, and male friends tended to be similar in their consumption of high-calorie foods. Popularity (receiving ties) was also associated with some behaviors, although these effects were gender specific and differed across networks.     

Ethnic segregation of friendship networks in school: Testing a rational-choice argument of differences in ethnic homophily between classroom- and grade-level networks

Adolescents’ school-based friendship networks tend to be segregated along ethnic lines. But few studies have examined whether variation in network boundaries affects the degree of ethnic friendship segregation. We use rational-choice theory to argue that ethnic homophily is more pronounced for friendships between classrooms than for those within classrooms. We empirically test this hypothesis using two-wave German panel data (N = 1258) and stochastic actor-oriented models (RSiena). In line with our theoretical argument, we find that the tendency to form same-ethnic friendships is indeed stronger at the grade level, which translates into stronger ethnic segregation in friendship networks at the grade level than at the classroom level. Implications for research on ethnic segregation in school-based friendship networks are discussed.     

The subjective dimension of social isolation: A qualitative investigation of older adults' experiences in small social support networks

In the aging literature, social isolation has been primarily defined in terms of reduced support network size and low frequency of social contacts. Having a small social support network is associated with social isolation and an increased risk of physical and emotional vulnerability. However, this conceptualization ignores the contributions of a host of other factors, including life experiences, family dynamics, and long-term patterns of socialization. This paper argues that alongside quantitative assessments of support systems, the application of a life course perspective is needed to understand small social networks as lived experience. We report on findings from 28 in-depth interviews with older adults identified as being at risk of social isolation on the basis of the self-reported size of their social networks. We discuss these participants' experiences in the context of significant life course transitions such as marriage and widowhood.     

A model for the multiplex dynamics of two-mode and one-mode networks,with an application to employment preference,friendship, and advice

We propose a new stochastic actor-oriented model for the co-evolution of two-mode and one-mode networks. The model posits that activities of a set of actors, represented in the two-mode network, co-evolve with exchanges and interactions between the actors, as represented in the one-mode network. The model assumes that the actors, not the activities, have agency.     

Beauty and social capital: Being attractive shapes social networks

It is a fact of life that being beautiful is advantageous (Hamermesh, 2011). Whether we consider incomes or the marriage market, the more attractive one is, the better one does. Drawing on psychological research that explains why beauty pays, we hypothesize that more attractive people will exploit social network opportunities differently than less attractive people and, consequently, their networks will comprise more beneficial features. As predicted, results of an experiment showed that more attractive people were more likely to select for themselves more profitable broker positions in networks relative to other positions and relative to less attractive people. Relying on network data supplied by young professionals, in a follow-up study we found that the networks of more attractive people were relatively less dense, as the findings of Study 1 would imply. We discuss the implications for our work as well as highlight the need for further research into an understudied, but potentially influential brokerage contingency factor—physical appeal.     

Learning in social networks: Selecting profitable choices among alternatives of uncertain profitability in various networks

Social capital theory assumes that information is valuable. However, only rarely is this value explicitly modeled, and there are few examples of empirical tests of mechanisms that connect social network structure to valuable information. We model an individual decision problem in which individuals make choices that yield uncertain outcomes. The individuals can learn about the profitability of options from their own choices and from the network. We generate computer-simulated data to derive hypotheses about the effect of network characteristics on making profitable choices. We conduct a laboratory experiment to empirically test these hypotheses and find that, at the individual level, degree centrality has a positive effect on making profitable choices whereas betweenness centrality has no effect. At the network level, density has a positive effect on making profitable choices, whereas centralization does not have an effect.     

Recent developments in exponential random graph (p*) models for social networks

This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology] and demonstrates their improvement over homogeneous Markov random graph models in fitting empirical network data. Not only do the new specifications show improvements in goodness of fit for various data sets, but they also help to avoid the problem of near-degeneracy that often afflicts the fitting of Markov random graph models in practice, particularly to network data exhibiting high levels of transitivity. The inclusion of a new higher order transitivity statistic allows estimation of parameters of exponential graph models for many (but not all) cases where it is impossible to estimate parameters of homogeneous Markov graph models. The new specifications were used to model a large number of classical small-scale network data sets and showed a dramatically better performance than Markov graph models. We also review three current programs for obtaining maximum likelihood estimates of model parameters and we compare these Monte Carlo maximum likelihood estimates with less accurate pseudo-likelihood estimates. Finally, we discuss whether homogeneous Markov random graph models may be superseded by the new specifications, and how additional elaborations may further improve model performance.     

Biased networks and social structure theorems

It is shown that the part of Peter Blau's theory of social structure bearing on heterogeneity can be imbedded within the framework of biased networks, thereby clarifying conceptual aspects of the theory as well as providing a mathematical proof of the most central heterogeneity theorem for a class of networks. The key step is the postulation of a mathematical concept of salience, called an ‘inbreeding bias’ in prior work, which corresponds to Blau's fundamental axiom. In addition, there are derivations and discussions bearing on related topics in the analysis of social structure.     

An introduction to exponential random graph (p) models for social networks

This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov random graph models of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponential random graph models. Sociological Methodology, in press] offer substantial improvement.     

The contribution of the intervention in social networks and community social work at the local level to social and human development

ABSTRACT

The primary task of community social work is building social networks by reinforcing people's resources and those of the different environmental and social contexts from three dimensions: personal development, social development and organisational development. The new information technologies today establish a relationship of communication with local communities and citizens that promotes proximity to social networks. Social intervention is supported by a set of methods from human geography that can be used as tools to create maps of the territory and the networks for planning, diagnosing and classifying the management of community network intervention. In this discussion we set out to analyse the contribution of the intervention in social networks as a means of achieving a new configuration of social networks at the local level. This information is obtained from semi-structured interviews with social workers and other professionals in the social sphere in municipalities with over 100,000 inhabitants in the Madrid region (Spain). The research results show that intervention in social networks locally multiplies the opportunities to enhance the quality of people's social relationships, thus expanding their social support by strengthening their bonds, their personal network and support systems; secondly, it increases empowerment to facilitate a type of intervention to strengthen human potential and to gain autonomy and full citizenship.     

A comparison of email networks and off-line social networks: A study of a medium-sized bank

Recently there has been a surge in the availability of online data concerning the connections between people, and these online data are now widely used to map the social structure of communities. There has been little research, however, on how these new types of relational data correspond to classical measures of social networks. To fill this gap, we contrast the structure of an email network with the underlying friendship, communication, and advice seeking networks. Our study is an explorative case study of a bank, and our data contains emails among employees and a survey of the ego networks of the employees. Through calculating correlations with QAP standard errors and estimating exponential random graph (ERG) models, we find that although the email network is related to the survey-based social networks, email networks are also significantly different: while off-line social networks are strongly shaped by gender, tenure, and hierarchical boundaries, the role of these boundaries are much weaker in the email network.     

Affinity Paths and information diffusion in social networks

Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different contents. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants’ involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the Affinity Paths observed in real information diffusion cascades.     

Gender,rank, and social networks on an enterprise social media platform

In this paper, we harness server-side data—540,000 messages generated by 2085 users on TamTamy, an Enterprise Social Media (ESM) platform—to examine how gender and rank shaped “homophily” (the tendency to connect with similar others) and centrality in an ESM network. Drawing on the logic of “distinctiveness theory,” which argues that the numeric rarity of a category in a given setting promotes the use of that category as a basis for connecting with others, we hypothesized and found: (a) the tendency to connect with same-gender others was stronger among women than among men; (b) the tendency to connect with same-rank others was stronger among high-ranking employees than among low-ranking employees; (c) for high-ranking men, rank was more important than gender as a basis for connecting with others; and (d) for low-ranking women, gender was more important than rank as a basis for connecting with others. We also found that whereas higher ranking individuals were more likely to be in central (bridging) positions in the overall network, gender was unrelated to network centrality. Our study suggests that the affordances of ESM for open and distributed communications notwithstanding, the social networks that emerge on ESM platforms may reinforce social stratification on some dimensions while diminishing it on others.     

Power and the perception of social networks

Previous work has led to divergent conclusions about how power affects the accuracy of network perceptions in groups and organizations. This paper develops and tests an argument linking higher power to less accurate network perception. Results from the first experiment showed that, relative to participants primed with high power, those primed with low power had more accurate perceptions of who was tied to whom in novel networks. The second experiment demonstrated that such differences in perceptual accuracy do not emerge for non-social relations.     

Assessing structural correlates to social capital in Facebook ego networks

Research in computer-mediated communication has consistently asserted that Facebook use is positively correlated with social capital. This research has drawn primarily on Williams’ (2006) bridging and bonding scales as well as behavioral attributes such as civic engagement. Yet, as social capital is inherently a structural construct, it is surprising that so little work has been done relating social capital to social structure as captured by social network site (SNS) Friendship networks. Facebook is particularly well-suited to support the examination of structure at the ego level since the networks articulated on Facebook tend to be large, dense, and indicative of many offline foci (e.g., coworkers, friends from high school). Assuming that each one of these foci only partially overlap, we initially present two hypotheses related to Facebook social networks and social capital: more foci are associated with perceptions of greater bridging social capital and more closure is associated with greater bonding social capital. Using a study of 235 employees at a Midwestern American university, we test these hypotheses alongside self-reported measures of activity on the site. Our results only partially confirm these hypotheses. In particular, using a widely used measure of closure (transitivity) we observe a strong and persistent negative relationship to bonding social capital. Although this finding is initially counter-intuitive it is easily explained by considering the topology of Facebook personal networks: networks with primarily closed triads tend to be networks with tightly bound foci (such as everyone from high school knowing each other) and few connections between foci. Networks with primarily open triads signify many crosscutting friendships across foci. Therefore, bonding social capital appears to be less tied to local clustering than to global cohesion.     

Universal evolution patterns of degree assortativity in social networks

Degree assortativity characterizes the propensity for large-degree nodes to connect to other large-degree nodes and low-degree to low-degree. It is important to describe the forces forming the network and to predict the behavior of dynamic systems on the network. To understand the evolutionary dynamics of degree assortativity, we collect a variety of empirical temporal social networks, and find that there is a universal pattern that the degree assortativity increases at the beginning of evolution and then decreases to a long-lasting stable level. We develop a bidirectional selection model to re-construct the evolution dynamic. In our model, we assume each individual has a social status that—in analogy to Pareto’s wealth distribution —follows a power-law distribution. We assume the social status determines the probability of an interaction between two actors. By varying the ratio of link establishment from within the same status level to across different status levels, the simulated network can be tuned to be assortative or disassortative. This suggests that the rise-and-fall pattern of degree assortativity is a consequence of the different network-forming forces active at different mixing of status. Our simulations indicate that Pareto social status distribution in the population may drive the social evolution in a way of self-optimization to promote the social interaction among individuals and the status gap plays an important role for the assortativity of the social network.