Occam Factor for Random Graphs: Erdős-Rényi, Independent Edge, and a Uniparametric Stochastic Blockmodel

We investigate the evidence/flexibility (i.e., "Occam") paradigm and demonstrate the theoretical and empirical consistency of Bayesian evidence for the task of determining an appropriate generative model for network data. This model selection framework involves determining a collection of candidate models, equipping each of these models' parameters with prior distributions derived via the encompassing priors method, and computing or approximating each models' evidence. We demonstrate how such a criterion may be used to select the most suitable model among the Erd\H{o}s-R\'enyi (ER) model, independent edge (IE) model, and a special one-parameter low-rank stochastic blockmodel (SBM) with known memberships. The Erd\H{o}s-R\'enyi may be considered as being linearly nested within IE, a fact which permits exponential family results. The uniparametric SBM is not so ideal, so we propose a numerical method to approximate the evidence. We apply this paradigm to brain connectome data. Future work necessitates deriving and equipping additional candidate random graph models with appropriate priors so they may be included in the paradigm.