方法对比
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| 多层随机块模型× | 贝叶斯随机块模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015-2017 | 2001–2014 |
| 提出者≠ | Peixoto, T. P.; De Bacco, C. and colleagues | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. |
| 类型≠ | Generative probabilistic model | Probabilistic generative model with Bayesian inference |
| 开创性文献≠ | Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807. DOI ↗ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ |
| 别名 | ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Multilayer Stochastic Block Model (ML-SBM) is a generative probabilistic framework that extends the classical stochastic block model to networks with multiple relation types or layers. It simultaneously infers community structure and block-to-block connection probabilities across all layers, capturing how communities cohere differently depending on context or relationship type. | 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. |
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