Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическая модель экспоненциального случайного графа× | Временной анализ сетей× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство≠ | Machine learning | Process / pipeline |
| Год появления≠ | 2010–2014 | 2012 |
| Автор метода≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Holme & Saramäki (2012) — seminal framework |
| Тип≠ | Probabilistic graphical model (temporal) | Dynamic graph analysis |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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 данных ↗ |
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