Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Modularity ya Muda× | Uchanganuzi wa Mitandao ya Kijamii ya Muda× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2010 | 2000s–2010s |
| Mwanzilishi≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. | Moody, J.; Holme, P.; Saramäki, J. |
| Aina≠ | Community detection (temporal extension of modularity optimization) | Longitudinal network analysis |
| Chanzo asilia≠ | 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 ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Majina mbadala | dynamic modularity, time-varying modularity, longitudinal community detection, temporal community structure analysis | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
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