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| Dynamic Community Detection× | Временно откриване на общности× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2010 (key formalization); earlier work 2002–2009 | 2010 |
| Създател≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | Mucha, P. J. et al. |
| Тип≠ | Graph clustering / community discovery | Network clustering algorithm |
| Основополагащ източник | 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 ↗ |
| Други названия | DCD, temporal community detection, evolving community detection, dynamic graph clustering | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Свързани≠ | 5 | 6 |
| Резюме≠ | Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research. | 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. |
| ScholarGateНабор от данни ↗ |
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