Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Dynamický model exponenciálních náhodných grafů× | Analýza temporálních sítí× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina≠ | Machine learning | Process / pipeline |
| Rok vzniku≠ | 2010–2014 | 2012 |
| Tvůrce≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Holme & Saramäki (2012) — seminal framework |
| Typ≠ | Probabilistic graphical model (temporal) | Dynamic graph analysis |
| Původní zdroj≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Další názvy≠ | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Příbuzné≠ | 4 | 3 |
| Shrnutí≠ | 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. | Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system. |
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