Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Centralitat de grau temporal× | Centralitat de Betweenness Temporal× | |
|---|---|---|
| Camp | Anàlisi de xarxes | Anàlisi de xarxes |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2011–2012 | 2012 |
| Autor original≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. |
| Tipus≠ | Centrality measure (temporal extension) | Centrality measure for temporal networks |
| Font 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 ↗ |
| Àlies | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness |
| Relacionats | 6 | 6 |
| Resum≠ | 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. |
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