Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Detecção de Comunidades Temporais× | Análise de Redes Temporais× | |
|---|---|---|
| Área | Análise de redes | Análise de redes |
| Família≠ | Machine learning | Process / pipeline |
| Ano de origem≠ | 2010 | 2012 |
| Autor original≠ | Mucha, P. J. et al. | Holme & Saramäki (2012) — seminal framework |
| Tipo≠ | Network clustering algorithm | Dynamic graph analysis |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes≠ | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Relacionados≠ | 6 | 3 |
| Resumo≠ | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. | 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 ↗ |
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