Bayesian Model Selection of Stochastic Block Models

Understand

A central problem in analyzing networks is partitioning them into modules or communities.

• One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links.
• Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model.
• Here we propose a Bayesian framework for choosing the number of blocks as well as comparing it to the more elaborate degree- corrected block models, ultimately leading to a universal model selection framework capable of comparing multiple modeling combinations.