Multilevel longitudinal analysis of social networks

Local Network Interaction as a Mechanism for Wealth Inequality

Given limited institutional resources, low-income populations often rely on social networks to improve their socioeconomic outcomes. However, it remains in question whether small-scale social interactions could affect large-scale economic inequalities in under-resourced contexts. Here, we leverage population-level data from one of the poorest South African settings to construct a large-scale, geographically defined, inter-household social network. Using a multilevel network model, we show that having social ties in close geographic proximity is associated with stable household asset conditions, while geographically distant ties correlate to changes in asset allocation. Notably, we find that localised network interactions are associated with an increase in wealth inequality at the regional level, demonstrating how macro-level inequality may arise from micro-level social processes. Our findings highlight the importance of understanding complex social connections underpinning inter-household resource dynamics, and raise the potential of large-scale social assistance programs to reduce disparities in resource-ownership by accounting for local social constraints.

The dynamics of intergenerational closure and family networks of social cohesion

We investigate the correlation of ties among school-children's parents with violence in schools, and two mechanisms of intergenerational closure (IC). Coleman described ties among parents of befriended children as IC. Until now, IC indicated social capital in schools and neighborhoods, but existing evidence is rather ambiguous and does not utilize network data. According to "top-down." IC, children establish network ties because of the acquaintance among their parents. "Bottom-up" IC implies that children make friends first and then their parents get involved. We use longitudinal social network data from k = 10 school classes and N = 238 adolescents and disentangle the two different dynamics of IC by applying Bayesian stochastic actor-oriented models (SAOMs) for network evolution. SAOMs show positive "top-down" and "bottom-up" effects on IC, with the latter being considerably stronger.

Multilevel network interventions: Goals, actions, and outcomes

COVID-19 has resulted in dramatic and widespread social network interventions across the globe, with public health measures such as distancing and isolation key epidemiological responses to minimize transmission. Because these measures affect social interactions between people, the networked structure of daily lives is changed. Such largescale changes to social structures, present simultaneously across many different societies and touching many different people, give renewed significance to the conceptualization of social network interventions. As social network researchers, we need a framework for understanding and describing network interventions consistent with the COVID-19 experience, one that builds on past work but able to cast interventions across a broad societal framework. In this theoretical paper, we extend the conceptualization of social network interventions in these directions. We follow Valente (2012) with a tripartite categorization of interventions but add a multilevel dimension to capture hierarchical aspects that are a key feature of any society and implicit in any network. This multilevel dimension distinguishes goals, actions, and outcomes at different levels, from individuals to the whole of the society. We illustrate this extended taxonomy with a range of COVID-19 public health measures of different types and at multiple levels, and then show how past network intervention research in other domains can also be framed in this way. We discuss what counts as an effective network, an effective intervention, plausible causality, and careful selection and evaluation, as central to a full theory of network interventions.