Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическая центральность по степени× | Временной анализ сетей× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство≠ | Machine learning | Process / pipeline |
| Год появления | 2012 | 2012 |
| Автор метода≠ | Holme, P. & Saramaki, J.; Kim, H. & Anderson, R. | Holme & Saramäki (2012) — seminal framework |
| Тип≠ | Centrality measure (temporal extension) | Dynamic graph analysis |
| Основополагающий источник≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Другие названия≠ | time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Связанные≠ | 5 | 3 |
| Сводка≠ | Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network. | 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 ↗ |
|
|