  Assessing the model fit quality of statistical models for network data is an
ongoing and under-examined topic in statistical network analysis. Traditional
metrics for evaluating model fit on tabular data such as the Bayesian
Information Criterion are not suitable for models specialized for network data.
We propose a novel self-developed goodness of fit (GOF) measure, the
`stratified-sampling cross-validation' (SCV) metric, that uses a procedure
similar to traditional cross-validation via stratified-sampling to select dyads
in the network's adjacency matrix to be removed. SCV is capable of intuitively
expressing different models' ability to predict on missing dyads. Using SCV on
real-world social networks, we identify the appropriate statistical models for
different network structures and generalize such patterns. In particular, we
focus on conditionally independent dyad (CID) models such as the Erdos Renyi
model, the stochastic block model, the sender-receiver model, and the latent
space model.
