Abstract: Estimating indirect effects (spillovers) requires accurate knowledge of the network of possible influence pathways. In practice, however, networks are often measured with error. Errors arise from sources such as respondents' memory lapses, researchers collecting networks at baseline when connections form during the experimental period, or researchers limiting the number of connections a respondent can nominate. Further, network surveys are often prohibitively expensive, so researchers proxy the network via other methods (e.g. defining a possible spillover pathway if individuals are in the same geographic area, in the same classroom in a school, etc.). In this paper we consider the setting where measured connections are a noisy representation of the true pathways of treatment interference. We show that existing methods yield biased estimators in the presence of this mismeasurement, then develop a class of mixture models that account for missing connections and discuss estimation via the Expectation-Maximization (EM) algorithm. We evaluate its performance by simulating experiments on realistic networks and implement our method using data from two published studies: one where networks are constructed using same class as a proxy, and the other where the number of contacts is censored. In both cases incorporating mismeasurement leads to larger treatment effect estimates. This is joint work with Wesley Lee (UW) and Morgan Hardy (NYU Abu Dhabi).