方法对比
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| 加权时间网络分析× | 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≠ | 2004–2012 | 2004–2008 |
| 提出者≠ | Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks) | Newman, M. E. J.; Blondel et al. |
| 类型≠ | Network analysis technique | Graph clustering / community detection |
| 开创性文献≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. 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 | WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD |
| 相关 | 6 | 6 |
| 摘要≠ | Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment. | 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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