方法对比
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| 时间网络扩散分析× | 时态社群检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2012 | 2010 |
| 提出者≠ | Holme, P. & Saramäki, J. | Mucha, P. J. et al. |
| 类型≠ | Network analysis framework | Network clustering algorithm |
| 开创性文献≠ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ |
| 别名 | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 相关≠ | 5 | 6 |
| 摘要≠ | Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
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