Machine learningNetwork science
加权随机块模型
加权随机块模型 (W-SBM) 将经典的随机块模型扩展到其边带有数值权重的网络。通过假设节点对之间的边权重来自依赖于这些节点块成员身份的分布，它可以同时推断节点到社区的划分以及一组块到块的权重参数——恢复了非加权方法无法看到的结构。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248. DOI: 10.1093/comnet/cnu026 ↗
- Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087. DOI: 10.1198/016214501753208735 ↗
如何引用本页
ScholarGate. (2026, June 3). Weighted Stochastic Block Model (W-SBM). ScholarGate. https://scholargate.app/zh/network-analysis/weighted-stochastic-block-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 模块度分析网络分析↔ compare
- 随机块模型网络分析↔ compare
- 加权社区检测网络分析↔ compare
- 加权指数随机图模型网络分析↔ compare
- 加权模块度分析网络分析↔ compare
- 加权社会网络分析网络分析↔ compare