Imputing Missing Network Data

Missing data on network ties is a fundamental problem for network analyses. The biases induced by missing edge data, even when missing completely at random (MCAR), are widely acknowledged (Kossinets, 2006; Huisman & Steglich, 2008; Huisman, 2009). Although model based techniques for missing network data are quite promising, they are not available for all analyses (Koskinen, Robins & Pattison, 2010). Multiple imputation for network data is able to overcome this problem. This study expands on recent work on multiple imputation of missing data in networks with extensive simulations (Wang et al. 2016). Different models for imputing the missing data are compared under 64 conditions