Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Динамічна модель експоненційних випадкових графів× | Аналіз часових мереж× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина≠ | Machine learning | Process / pipeline |
| Рік появи≠ | 2010–2014 | 2012 |
| Автор методу≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Holme & Saramäki (2012) — seminal framework |
| Тип≠ | Probabilistic graphical model (temporal) | Dynamic graph analysis |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Пов'язані≠ | 4 | 3 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
|
|