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• 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. [ABSTRACT FROM AUTHOR]