Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling

TL;DR

This paper introduces a method to infer hidden social networks from respondent-driven sampling data by modeling recruitment as a diffusion process and optimizing network reconstruction, aiding epidemiological research.

Contribution

The authors develop RENDER, an efficient algorithm to reconstruct underlying social networks from RDS data, addressing privacy constraints and limited observed information.

Findings

RENDER accurately reconstructs networks in synthetic experiments.

The method successfully infers real-world social structures from RDS data.

The approach enhances understanding of hidden populations' social connectivity.

Abstract

Learning about the social structure of hidden and hard-to-reach populations --- such as drug users and sex workers --- is a major goal of epidemiological and public health research on risk behaviors and disease prevention. Respondent-driven sampling (RDS) is a peer-referral process widely used by many health organizations, where research subjects recruit other subjects from their social network. In such surveys, researchers observe who recruited whom, along with the time of recruitment and the total number of acquaintances (network degree) of respondents. However, due to privacy concerns, the identities of acquaintances are not disclosed. In this work, we show how to reconstruct the underlying network structure through which the subjects are recruited. We formulate the dynamics of RDS as a continuous-time diffusion process over the underlying graph and derive the likelihood for the recruitment time series under an arbitrary recruitment time distribution. We develop an efficient stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork Reconstruction) that finds the network that best explains the collected data. We support our analytical results through an exhaustive set of experiments on both synthetic and real data.