  A general Bayesian framework for model selection on random network models
regarding their features is considered. The goal is to develop a principle
Bayesian model selection approach to compare different fittable, not
necessarily nested, models for inference on those network realisations. The
criterion for random network models regarding the comparison is formulated via
Bayes factors and penalizing using the mostwidely used loss functions.
Parametrizations are different in different spaces. To overcome this problem we
incorporate and encode different aspects of complexities in terms of observable
spaces. Thus, given a range of values for a feature, network realisationsare
extracted. The proposed principle approach is based on finding random network
models, such that a reasonable trade off between the interested feature and the
complexity of the model is preserved, avoiding overfitting problems.
