Title: Sampling from Social Networks: A Bayesian Perspective
Abstract: Consider a population of individuals and a network that encodes social connections among them. We are interested in making inference on super-population estimands that are a function of both individuals’ responses and of the network, from a sample. Neither the sampling frame nor the full network are available. However, the sampling mechanism implicitly leverages the network to recruit individuals, thus partially revealing social interactions among the individuals in the sample, as well as their responses. This is a common setting that arises, for instance, in epidemiology and healthcare, where samples from hard-to-reach populations are collected using link-tracing mechanisms, including respondent-driven sampling. Contrary to random sampling, the probability models of these network sampling mechanisms carry information about the estimands of interest, such as the incidence of certain diseases in the target population. In this work, we identify key modeling elements and propose a modelling framework based on them. We are able to provide general guidelines in terms of modelling and Bayesian computation for this type of problems. We show the usefulness of this formulation on a study of the incidence of HIV in Brazil. Joint work with Edoardo M. Airoldi