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Assessing the model fit quality of statistical models for network data is anongoing and under-examined topic in statistical network analysis. Traditionalmetrics for evaluating model fit on tabular data such as the BayesianInformation 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 proceduresimilar to traditional cross-validation via stratified-sampling to select dyadsin the network's adjacency matrix to be removed. SCV is capable of intuitivelyexpressing different models' ability to predict on missing dyads. Using SCV onreal-world social networks, we identify the appropriate statistical models fordifferent network structures and generalize such patterns. In particular, wefocus on conditionally independent dyad (CID) models such as the Erdos Renyimodel, the stochastic block model, the sender-receiver model, and the latentspace model.

CID Models on Real-world Social Networks and Goodness of Fit Measurements