Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамический анализ эго-сетей× | Временной анализ сетей× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство≠ | Machine learning | Process / pipeline |
| Год появления≠ | 1990s–2015 | 2012 |
| Автор метода≠ | Burt, R. S.; Wellman, B. (foundational ego-net); dynamic extension developed across the 1990s–2010s | Holme & Saramäki (2012) — seminal framework |
| Тип≠ | Longitudinal network analysis framework | Dynamic graph analysis |
| Основополагающий источник≠ | Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. ISBN: 978-0-674-84372-1 | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Другие названия≠ | longitudinal ego network analysis, temporal ego network analysis, personal network dynamics, dynamic personal network analysis | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Связанные | 3 | 3 |
| Сводка≠ | Dynamic ego network analysis examines how the personal network surrounding a focal individual (the ego) changes over time. By collecting the same ego-centered network data at multiple time points, researchers can track tie formation and dissolution, shifts in network composition, and changes in structural properties such as density, constraint, and network size — and link these dynamics to individual outcomes. | 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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