detect communities using weighted stochastic blockmodel
MATLAB code that calls a functions for fitting a weighted stochastic blockmodel to network data. As an example, we show an application to a functional connectivity matrix.
Unlike Infomap and modularity maximization, which are the two most common community detection methods used in network neuroscience, the WSBM is capable of detecting more general classes of communities, rather than the traditional internally dense and externally sparse assortative organization.
If you use this code, please cite our papers:
Betzel, R. F., Medaglia, J. D., & Bassett, D. S. (2018). Diversity of meso-scale architecture in human and non-human connectomes. Nature communications, 9(1), 346.
and
Betzel, R. F., Bertolero, M. A., & Bassett, D. S. (2018). Non-assortative community structure in resting and task-evoked functional brain networks. bioRxiv, 355016.