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| Temporale Nähe-Zentralität× | Betweenness-Zentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2011 | 1977 |
| Urheber≠ | Pan, R. K. & Saramaki, J. | Freeman, L. C. |
| Typ≠ | Centrality measure (temporal) | Centrality measure |
| Wegweisende Quelle≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Aliasnamen | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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