Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Динамичен модел на случайни графи с експоненциално разпределение× | Анализ на времеви мрежи× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство≠ | Machine learning | Process / pipeline |
| Година на възникване≠ | 2010–2014 | 2012 |
| Създател≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Holme & Saramäki (2012) — seminal framework |
| Тип≠ | Probabilistic graphical model (temporal) | Dynamic graph analysis |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Свързани≠ | 4 | 3 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|