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| Dinamikus közösségdetektálás× | Időtartam-közösségdetektálás× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2010 (key formalization); earlier work 2002–2009 | 2010 |
| Megalkotó≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | Mucha, P. J. et al. |
| Típus≠ | Graph clustering / community discovery | Network clustering algorithm |
| Alapmű | 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 ↗ |
| Alternatív nevek | DCD, temporal community detection, evolving community detection, dynamic graph clustering | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Kapcsolódó≠ | 5 | 6 |
| Összefoglaló≠ | 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. |
| ScholarGateAdatkészlet ↗ |
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