Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Ajaline multipleksvõrgu analüüs× | Ajaline võrguanalüüs× | |
|---|---|---|
| Valdkond | Võrgustikuanalüüs | Võrgustikuanalüüs |
| Perekond≠ | Machine learning | Process / pipeline |
| Tekkeaasta≠ | 2012–2014 | 2012 |
| Looja≠ | Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors) | Holme & Saramäki (2012) — seminal framework |
| Tüüp≠ | Structural and dynamic network analysis | Dynamic graph analysis |
| Algallikas≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Rööpnimetused≠ | TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Seotud≠ | 5 | 3 |
| Kokkuvõte≠ | Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods. | 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. |
| ScholarGateAndmestik ↗ |
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