方法对比
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| 贝叶斯随机块模型× | 随机块模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2001–2014 | 1983 |
| 提出者≠ | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. | — |
| 类型≠ | Probabilistic generative model with Bayesian inference | Probabilistic generative graph model |
| 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 ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 别名 | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 相关≠ | 5 | 7 |
| 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. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
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