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| Multilayer Temporal Network Analysis× | Zeitliche Netzwerkanalyse× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie≠ | Machine learning | Process / pipeline |
| Entstehungsjahr≠ | 2012–2014 | 2012 |
| Urheber≠ | Kivela, M. et al.; Holme, P. & Saramaki, J. | Holme & Saramäki (2012) — seminal framework |
| Typ≠ | Network analysis framework | Dynamic graph analysis |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | MTNA, temporal multilayer network analysis, time-varying multilayer network analysis, dynamic multilayer network analysis | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Multilayer temporal network analysis studies relational systems in which nodes interact through multiple distinct types of ties that all evolve over time. By modeling each relationship type as a separate layer and tracking how those layers change across time snapshots, the method reveals how cross-layer dynamics and temporal patterns jointly shape information flow, influence spread, and community structure. | 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. |
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