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| 动态模块性分析× | 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 | 2010 | 2010 |
| 提出者≠ | Mucha, P. J.; Porter, M. A.; and colleagues | Mucha, P. J. et al. |
| 类型≠ | Community detection on temporal networks | Network clustering algorithm |
| 开创性文献 | 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 ↗ | 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 | dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 相关≠ | 5 | 6 |
| 摘要≠ | Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data. | 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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