Bayesian Model Selection of Stochastic Block Models
This addresses a key statistical challenge for researchers analyzing network communities, but it appears incremental as it builds on existing stochastic block model methods.
The authors tackled the problem of selecting the number of blocks in stochastic block models for network analysis by proposing a Bayesian framework, which enables principled model selection and comparison with degree-corrected variants, though no concrete numerical results are provided in the abstract.
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. We will also investigate its connection to the minimum description length principle.