Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Centralidade de Grau Temporal× | Centralidade de Intermediação Temporal× | |
|---|---|---|
| Área | Análise de redes | Análise de redes |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2011–2012 | 2012 |
| Autor original≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. |
| Tipo≠ | Centrality measure (temporal extension) | Centrality measure for temporal networks |
| Fonte seminal≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Outros nomes | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness |
| Relacionados | 6 | 6 |
| Resumo≠ | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. | Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot. |
| ScholarGateConjunto de dados ↗ |
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