方法对比
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| 加权社区检测× | 加权社会网络分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2004–2008 | 2004–2010 |
| 提出者≠ | Newman, M. E. J.; Blondel et al. | Barrat, A.; Opsahl, T. et al. |
| 类型≠ | Graph clustering / community detection | Network analysis framework |
| 开创性文献≠ | 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 ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| 别名 | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. |
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