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| 加权随机块模型× | 加权模块度分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 | 2004 |
| 提出者≠ | Aicher, C.; Jacobs, A. Z.; Clauset, A. | Newman, M. E. J. |
| 类型≠ | Generative probabilistic model | Community structure optimization on weighted graphs |
| 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 ↗ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ |
| 别名 | W-SBM, weighted SBM, weighted block model, weighted community detection via SBM | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity |
| 相关≠ | 6 | 5 |
| 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. | Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully. |
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