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

Authors

  • Lin Chen Yale University
  • Forrest Crawford Yale University
  • Amin Karbasi Yale University

DOI:

https://doi.org/10.1609/aaai.v30i1.10164

Keywords:

respondent-driven sampling, network reconstruction, machine learning

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 of the recruitment time series under an arbitrary inter-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.

Downloads

Published

2016-02-21

How to Cite

Chen, L., Crawford, F., & Karbasi, A. (2016). Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10164

Issue

Section

Technical Papers: Machine Learning Applications