Exact Likelihood Inference for Snowball-Sampled Erdős-Rényi Networks

Abstract

Network data obtained through link-tracing designs, such as snowball sampling, are collected through a mechanism that depends on the very structure the analysis seeks to estimate. Ignoring this dependence and treating the observed sample as though it were itself a complete network can lead to substantially biased inference. While the resulting selection problem is intractable in general, we show that it admits an exact solution for $r$-wave snowball samples, with full-neighbourhood recruitment, drawn from an Erdős--Rényi population. We derive the exact likelihood of such a sample and show that it defines a curved exponential family in the edge probability $\pi$, with a low-dimensional sufficient statistic. Building on this result, we obtain the maximum likelihood estimator of $\pi$ that correctly accounts for the sampling design and, as a function of the minimal sufficient statistic, makes full use of the information in the sample. Simulation studies show that this correction substantially reduces bias relative to the naive estimator, remaining effectively unbiased even when the sample covers as little as 0.1\% of the network. We further construct valid confidence intervals for $\pi$ by inverting a test built on the exact sampling distribution, approximated via Monte Carlo simulation. Simulation studies confirm that these confidence intervals attain the nominal coverage level within Monte Carlo error across a range of edge probabilities and numbers of waves.