Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Dynamic Stochastic Block Model (DSBM)× | Analiza sieci czasowych× | |
|---|---|---|
| Dziedzina | Analiza sieci | Analiza sieci |
| Rodzina≠ | Machine learning | Process / pipeline |
| Rok powstania≠ | 2011 | 2012 |
| Twórca≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Holme & Saramäki (2012) — seminal framework |
| Typ≠ | Generative probabilistic model | Dynamic graph analysis |
| Źródło pierwotne≠ | Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Inne nazwy≠ | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time 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. |
| ScholarGateZbiór danych ↗ |
|
|