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| Dynamic Community Detection× | Анализ на модулността× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2010 (key formalization); earlier work 2002–2009 | 2004 |
| Създател≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | Newman, M. E. J. & Girvan, M. |
| Тип≠ | Graph clustering / community discovery | Community detection / graph partitioning |
| 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 ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Други названия | DCD, temporal community detection, evolving community detection, dynamic graph clustering | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Свързани | 5 | 5 |
| 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. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
| ScholarGateНабор от данни ↗ |
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