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| Gewichtete Zwischenzentralität× | Gewichtete Gradzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2010 | 2004 |
| Urheber≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. |
| Typ≠ | Centrality measure (path-based) | Centrality measure for weighted networks |
| Wegweisende Quelle≠ | Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| Aliasnamen | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) | node strength, strength centrality, weighted node degree, WDC |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters. | Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score. |
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