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| 加权模块度分析× | 加权社区检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2004 | 2004–2008 |
| 提出者≠ | Newman, M. E. J. | Newman, M. E. J.; Blondel et al. |
| 类型≠ | Community structure optimization on weighted graphs | Graph clustering / community detection |
| 开创性文献≠ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ | Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI ↗ |
| 别名 | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation. |
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