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| Временно откриване на общности× | Социален мрежов анализ× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2010 | 1934 (sociometry); 1994 (modern formalization) |
| Създател≠ | Mucha, P. J. et al. | Moreno, J.L.; formalized by Wasserman & Faust |
| Тип≠ | Network clustering algorithm | Structural/relational analysis framework |
| 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Други названия | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | SNA, network analysis, sociometric analysis, relational analysis |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
| ScholarGateНабор от данни ↗ |
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