Imputation of attributes in networked data using Bayesian autocorrelation regression models

•Autocorrelation regression models are useful for imputation of network attributes.•Bayesian cut models outperform straightforward Bayesian inference in network imputation.•Full Bayes fails under model misspecification and large amounts of missing data.•Inference can improve by changing the inference procedure without improving the model. Misspecification in network autocorrelation models poses a challenge for parameter estimation, which is amplified by missing data. Model misspecification has been a focus of recent work in the statistics literature and new robust procedures have been developed, in particular cutting feedback. This paper shows how this helps in a misspecified network autocorrelation model. Where model misspecification is mild and the traits are fully observed, Bayesian imputation is routine. In settings with high missingness, Bayesian inference can fail, but a closely related cut model is robust. We illustrate this on a data set of graduate students using a Facebook-like messaging app.