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| Времеви анализ на социални мрежи× | Временно откриване на общности× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2000s–2010s | 2010 |
| Създател≠ | Moody, J.; Holme, P.; Saramäki, J. | Mucha, P. J. et al. |
| Тип≠ | Longitudinal network analysis | Network clustering algorithm |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Свързани≠ | 4 | 6 |
| Резюме≠ | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. | 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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