Bayesian Core-Periphery Stochastic Block Models
Core-periphery structure is one of the most ubiquitous mesoscale patterns in networks. We have introduced two Bayesian stochastic block models, implemented in Python, that infer hub-and-spoke and layered core-periphery structures. The models can be used for probabilistic inference of core-periphery structure and model selection between the two core-periphery characterizations.
Details of the core-periphery models are detailed in our preprint:
Gallagher, Ryan J., Young, Jean-Gabriel, and Foucault Welles, Brooke. (2021). “A Clarified Typology of Core-Periphery Structure in Networks.” Science Advances.
Open-source implementations of the block models are available in Python on Github.