Clustering Network Tree Data From Respondent-driven sampling with application to opioid users in New York City

TL;DR

This paper develops a novel method for clustering tree-structured network data obtained through respondent-driven sampling, applied to opioid users in NYC to identify meaningful community subgroups for targeted interventions.

Contribution

It introduces an adjusted mixture model specifically designed for RDS-sampled tree network data, addressing the challenge of incomplete network information.

Findings

Identified distinct communities among opioid users in NYC.

Revealed social and drug use patterns relevant for intervention.

Demonstrated effectiveness of the model on real-world RDS data.

Abstract

There is great interest in finding meaningful subgroups of attributed network data. There are many available methods for clustering complete network. Unfortunately, much network data is collected through sampling, and therefore incomplete. Respondent-driven sampling (RDS) is a widely used method for sampling hard-to-reach human populations based on tracing links in the underlying unobserved social network. The resulting data therefore have tree structure representing a sub-sample of the network, along with many nodal attributes. In this paper, we introduce an approach to adjust mixture models for general network clustering for samplings by RDS. We apply our model to data on opioid users in New York City, and detect communities reflecting group characteristics of interest for intervention activities, including drug use patterns, social connections and other community variables