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| 加权随机块模型× | 加权指数随机图模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 | 2012 |
| 提出者≠ | Aicher, C.; Jacobs, A. Z.; Clauset, A. | Krivitsky, P. N. |
| 类型≠ | Generative probabilistic model | Statistical network 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 ↗ | Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128. DOI ↗ |
| 别名 | W-SBM, weighted SBM, weighted block model, weighted community detection via SBM | W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model |
| 相关≠ | 6 | 4 |
| 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 Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce. |
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