Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Analisis Modularitas Temporal× | Deteksi Komunitas Dinamis× | |
|---|---|---|
| Bidang | Analisis Jaringan | Analisis Jaringan |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2010 | 2010 (key formalization); earlier work 2002–2009 |
| Pencetus≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) |
| Tipe≠ | Community detection (temporal extension of modularity optimization) | Graph clustering / community discovery |
| Sumber perintis≠ | 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 ↗ |
| Alias | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis | DCD, temporal community detection, evolving community detection, dynamic graph clustering |
| Terkait | 5 | 5 |
| Ringkasan≠ | Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data. | 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. |
| ScholarGateSet data ↗ |
|
|