Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ временной модулярности× | Выявление временных сообществ× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления | 2010 | 2010 |
| Автор метода≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. | Mucha, P. J. et al. |
| Тип≠ | Community detection (temporal extension of modularity optimization) | Network clustering algorithm |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational 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. |
| ScholarGateНабор данных ↗ |
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