Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Mitandao ya Kijamii ya Muda× | Uchanganuzi wa Uenezaji wa Mtandao× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2000s–2010s | 1927 (epidemic roots); network formalization 1990s–2000s |
| Mwanzilishi≠ | Moody, J.; Holme, P.; Saramäki, J. | Kermack, W. O. & McKendrick, A. G. |
| Aina≠ | Longitudinal network analysis | Simulation / analytical model |
| Chanzo asilia≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721. DOI ↗ |
| Majina mbadala | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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. | Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally. |
| ScholarGateSeti ya data ↗ |
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