方法对比
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| 贝叶斯随机块模型× | 社群检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2001–2014 | 2002–2019 (algorithm family) |
| 提出者≠ | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| 类型≠ | Probabilistic generative model with Bayesian inference | Graph-partitioning / clustering algorithm family |
| 开创性文献≠ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ | Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI ↗ |
| 别名≠ | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| 相关 | 5 | 5 |
| 摘要≠ | The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches. | Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network? |
| ScholarGate数据集 ↗ |
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