Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo Estocástico de Blocos Dinâmico× | Análise de Redes Temporais× | |
|---|---|---|
| Área | Análise de redes | Análise de redes |
| Família≠ | Machine learning | Process / pipeline |
| Ano de origem≠ | 2011 | 2012 |
| Autor original≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Holme & Saramäki (2012) — seminal framework |
| Tipo≠ | Generative probabilistic model | Dynamic graph analysis |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes≠ | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Relacionados≠ | 5 | 3 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
|
|