A Python Package for Stochastic Block Model Inference
If you use this code, please cite
Funke T, Becker T (2019) Stochastic block models: A comparison of variants and inference methods. PLoS ONE 14(4): e0215296. https://doi.org/10.1371/journal.pone.0215296
in addition to the original authors of the respective methods (see below).
Our publication includes an introduction to the topic of Stochastic block models.
For a short introduction to this package see the Example.ipnyb.
We implement the stochastic block model variants from the following publications:
- Karrer B, Newman ME. Stochastic blockmodels and community structure in networks. Physical Review E. 2011; 83(1):016107. https://doi.org/10.1103/PhysRevE.83.016107
- Peixoto TP. Entropy of stochastic blockmodel ensembles. Physical Review E. 2012; 85(5):056122. https://doi.org/10.1103/PhysRevE.85.056122
- Côme E, Latouche P. Model selection and clustering in stochastic block models based on the exact inte- grated complete data likelihood. Statistical Modelling. 2015; 15(6):564–589. https://doi.org/10.1177/1471082X15577017
- Newman MEJ, Reinert G. Estimating the Number of Communities in a Network. Phys Rev Lett. 2016; 117:078301. https://doi.org/10.1103/PhysRevLett.117.078301 PMID: 27564002
- Peixoto TP. Hierarchical block structures and high-resolution model selection in large networks. Physi- cal Review X. 2014; 4(1):011047. https://doi.org/10.1103/PhysRevX.4.011047
- Peixoto TP. Nonparametric Bayesian inference of the microcanonical stochastic block model. Physical Review E. 2017; 95(1):012317. https://doi.org/10.1103/PhysRevE.95.012317 PMID: 28208453