ArticlePloS one2019
Stochastic block models: A comparison of variants and inference methods.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Structural covariance networks in schizophrenia: A systematic review Part II.Schizophrenia research · 2022Pooled it
- Gene regulatory network structure informs the distribution of perturbation effects.PLoS computational biology · 2025Article
- Analysing community-level spending behaviour contributing to high carbon emissions using stochastic block models.Scientific reports · 2025Article
- Community detection in the human connectome: Method types, differences and their impact on inference.Human brain mapping · 2024Article
- Spectral Clustering Community Detection Algorithm Based on Point-Wise Mutual Information Graph Kernel.Entropy (Basel, Switzerland) · 2023Article
- Article
- Identifying bias in network clustering quality metrics.PeerJ. Computer science · 2023Article
- Finite-state parameter space maps for pruning partitions in modularity-based community detection.Scientific reports · 2022Article
- Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model.Proceedings of machine learning research · 2021Article
- Neuronal classification from network connectivity via adjacency spectral embedding.Network neuroscience (Cambridge, Mass.) · 2021Article
- Covariance-based sample selection for heterogeneous data: Applications to gene expression and autism risk gene detection.Journal of the American Statistical Association · 2021Article
- Collective dynamics of stock market efficiency.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding communities in complex networks is a challenging task and one promising approach is the Stochastic Block Model (SBM). But the influences from various fields led to a diversity of variants and inference methods. Therefore, a comparison of the existing techniques and an independent analysis of their capabilities and weaknesses is needed. As a first step, we review the development of different SBM variants such as the degree-corrected SBM of Karrer and Newman or Peixoto's hierarchical SBM. Beside stating all these variants in a uniform notation, we show the reasons for their development. Knowing the variants, we discuss a variety of approaches to infer the optimal partition like the Metropolis-Hastings algorithm. We perform our analysis based on our extension of the Girvan-Newman test and the Lancichinetti-Fortunato-Radicchi benchmark as well as a selection of some real world networks. Using these results, we give some guidance to the challenging task of selecting an inference method and SBM variant. In addition, we give a simple heuristic to determine the number of steps for the Metropolis-Hastings algorithms that lack a usual stop criterion. With our comparison, we hope to guide researches in the field of SBM and highlight the problem of existing techniques to focus future research. Finally, by making our code freely available, we want to promote a faster development, integration and exchange of new ideas.
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