مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| شناسایی پویای جوامع× | تحلیل مدولاریتی× | |
|---|---|---|
| حوزه | تحلیل شبکه | تحلیل شبکه |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 2010 (key formalization); earlier work 2002–2009 | 2004 |
| پدیدآور≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | Newman, M. E. J. & Girvan, M. |
| نوع≠ | Graph clustering / community discovery | Community detection / graph partitioning |
| منبع بنیادین≠ | 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 ↗ |
| نامهای دیگر | DCD, temporal community detection, evolving community detection, dynamic graph clustering | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| مرتبط | 5 | 5 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
|
|