Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskais nejaušo grafu modelis (TERGM / STERGM)× | Tīkla difūzijas analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2010–2014 | 1927 (epidemic roots); network formalization 1990s–2000s |
| Autors≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Kermack, W. O. & McKendrick, A. G. |
| Tips≠ | Probabilistic graphical model (temporal) | Simulation / analytical model |
| Pirmavots≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. 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 ↗ |
| Citi nosaukumi | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | 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. |
| ScholarGateDatu kopa ↗ |
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