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| 加权随机块模型× | 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≠ | 2014 | 1983 |
| 提出者≠ | Aicher, C.; Jacobs, A. Z.; Clauset, A. | — |
| 类型≠ | Generative probabilistic model | Probabilistic generative graph model |
| 开创性文献≠ | Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 别名 | W-SBM, weighted SBM, weighted block model, weighted community detection via SBM | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 相关≠ | 6 | 7 |
| 摘要≠ | The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods. | 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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