Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Dynamisk modularitetsanalys× | Temporal nätverksanalys× | |
|---|---|---|
| Ämnesområde | Nätverksanalys | Nätverksanalys |
| Familj≠ | Machine learning | Process / pipeline |
| Ursprungsår≠ | 2010 | 2012 |
| Upphovsperson≠ | Mucha, P. J.; Porter, M. A.; and colleagues | Holme & Saramäki (2012) — seminal framework |
| Typ≠ | Community detection on temporal networks | Dynamic graph analysis |
| Ursprungskälla≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Alias≠ | dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Närliggande≠ | 5 | 3 |
| Sammanfattning≠ | Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data. | 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. |
| ScholarGateDatamängd ↗ |
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