Multiple Imputation for Missing Network Data

In this thesis we developed, implemented, and evaluated multiple imputation algorithms for missing network data. The algorithms are able to handle cross-sectional, longitudinal,and multiplex network structures, as well as nodal attributes (coevolving behaviors). They were implemented for the two most important statistical network model families in the social sciences, that is, Exponential Random Graph Models and Stochastic Actor-oriented Models