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| Gewichtete Netzwerkanalyse (Weighted Social Network Analysis)× | Betweenness-Zentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2004–2010 | 1977 |
| Urheber≠ | Barrat, A.; Opsahl, T. et al. | Freeman, L. C. |
| Typ≠ | Network analysis framework | Centrality measure |
| Wegweisende Quelle≠ | 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 ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Aliasnamen | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. | 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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