Practices of active citizenship among migrant youth: beyond conventionalities

This paper discusses the empirical manifestations of the notion of active citizenship in the context of the experiences of migrant youth. It focuses on the practices of active citizenship through involvement in social networks and creative civic engagement. In doing so, the article examines the complex and multi-faceted nature of social networking among migrant youth and the extent to which their approach to engagement is dependent on the specificities of the local environment, the type of social issues involved, and the cultural norms of one's own cultural heritage. Key empirical insights are derived from quantitative and qualitative research conducted among migrant youth of African, Arabic-speaking and Pacific Island backgrounds in Australia. These empirical insights are used to examine the changing perceptions of active citizenship among migrant youth, and the possibilities offered through non-traditional networks to engender civic engagement and social participation.     

Avoidance in negative ties: Inhibiting closure,reciprocity, and homophily

Theorising of negative ties has focused on simplex negative tie networks or multiplex signed tie networks. We examine the fundamental differences between positive and negative tie networks measured on the same set of actors. We test six mechanisms of tie formation on face-to-face positive (affect/esteem) and negative (dislike/disesteem) networks of 282 university students. While popularity, activity, and entrainment are present in both networks, closure, reciprocity, and homophily are largely absent from negative tie networks. We argue this arises because avoidance is inherent to negative sentiments. Avoidance reduces information transfer through negative ties and short-circuits cumulative causation.     

Bayesian Inference for Reliability of Systems and Networks Using the Survival Signature

The concept of survival signature has recently been introduced as an alternative to the signature for reliability quantification of systems. While these two concepts are closely related for systems consisting of a single type of component, the survival signature is also suitable for systems with multiple types of component, which is not the case for the signature. This also enables the use of the survival signature for reliability of networks. In this article, we present the use of the survival signature for reliability quantification of systems and networks from a Bayesian perspective. We assume that data are available on tested components that are exchangeable with those in the actual system or network of interest. These data consist of failure times and possibly right‐censoring times. We present both a nonparametric and parametric approach.     

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

Studies assume that socioeconomic status determines individuals’ states of health, but how does health determine socioeconomic status? And how does this association vary depending on contextual differences? To answer this question, our study uses an additive Bayesian Networks model to explain the interrelationships between health and socioeconomic determinants using complex and messy data. This model has been used to find the most probable structure in a network to describe the interdependence of these factors in five European welfare state regimes. The advantage of this study is that it offers a specific picture to describe the complex interrelationship between socioeconomic determinants and health, producing a network that is controlled by socio-demographic factors such as gender and age. The present work provides a general framework to describe and understand the complex association between socioeconomic determinants and health.     

It takes two: A dyadic model of recruitment to civic activity

Why are some people, but not others, asked to engage in civic activity? Rather than focus on the personal traits of either potential recruits or recruiters for this initial stage of recruitment, we develop and test a theoretical framework that emphasizes the importance of shared relationships and characteristics between those doing the recruiting and those being recruited. Specifically, the nature of interactions, overlapping community and associational space, status and value homophily, and strength and intimacy are assessed to explain differential recruitment among people's closest ties. Furthermore, unlike previous studies, we do so across three different forms of civic activity—blood donation, volunteer work, and political activism—allowing us to identify larger patterns in civic solicitation. Results from multilevel analyses of dyads reorganized from ego-centric data of U.S. adults show that while certain personal traits of egos and alters remain significant, attributes of the dyad are equally, if not more, consequential for explaining variation in who gets asked to participate in civic activity. Importantly, while certain dyadic characteristics—such as romantic partnerships—promote recruitment to all three forms of civic activity, the effects of others—such as sex homophily—are unique to specific forms. Broadly speaking, our results indicate that some types of dyadic characteristics are more powerful than others and that there are important differences in how particular dimensions of social connections shape recruitment efforts across the specific activities of donating blood, volunteering time, and engaging in political activism.     

Multilevel models for social networks: Hierarchical Bayesian approaches to exponential random graph modeling

In many applications, researchers may be interested in studying patterns of dyadic relationships that involve multiple groups, with a focus on modeling the systematic patterns within groups and how these structural patterns differ across groups. A number of different models – many of them potentially quite powerful – have been developed that allow for researchers to study these differences. However, as with any set of models, these are limited in ways that constrain the types of questions researchers may ask, such as those involving the variance in group-wise structural features. In this paper, we demonstrate some of the ways in which multilevel models based on a hierarchical Bayesian approach might be used to further develop and extend existing exponential random graph models to address such constraints. These include random coefficient extensions to the standard ERGM for sets of multiple unconnected or connected networks and examples of multilevel models that allow for the estimation of structural entrainment among connected groups. We demonstrate the application of these models to real-world and simulated data sets.     

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.     

A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model

In this article, we propose a class of logarithmic autoregressive conditional duration (ACD)-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and asymmetries in financial durations. In particular, our functional coefficient logarithmic autoregressive conditional duration (FC-LACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing sufficient conditions for strict stationarity, we address model identifiability as well as the asymptotic properties of the quasi-maximum likelihood (QML) estimator for the FC-LACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate a semiparametric variant of the FC-LACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations.     

Social fabric and fear of crime: Considering spatial location and time of day

Criminologists have long noted that social networks play a role in influencing residents' fear of crime, but findings vis a vis the exact nature of that role have been mixed. More social ties may be associated with less fear of crime through their role in collective action, trust, and emotional support, but also with more fear of crime because of their role in the diffusion of information on local crime patterns. In what follows, we suggest temporal and spatial distinctions in how social ties matter for fear of crime with respect to these different mechanisms. Analysis of data from a large scale egocentric network study in Southern California provides evidence for these claims.