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| 多层随机块模型× | 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≠ | 2015-2017 | 1983 |
| 提出者≠ | Peixoto, T. P.; De Bacco, C. and colleagues | — |
| 类型≠ | Generative probabilistic model | Probabilistic generative graph model |
| 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 ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 别名 | ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 相关≠ | 4 | 7 |
| 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 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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