方法对比
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| 时序随机块模型× | 多层随机块模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014–2017 | 2015-2017 |
| 提出者≠ | Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V. | Peixoto, T. P.; De Bacco, C. and colleagues |
| 类型 | Generative probabilistic model | Generative probabilistic model |
| 开创性文献≠ | Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141. DOI ↗ | Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807. DOI ↗ |
| 别名 | TSBM, dynamic stochastic block model, time-varying SBM, evolving block model | ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model |
| 相关 | 4 | 4 |
| 摘要≠ | The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time. | 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. |
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