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| Gewichtete Nähe-Zentralität× | Gewichtete Netzwerkanalyse (Weighted Social Network Analysis)× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2010 | 2004–2010 |
| Urheber≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Barrat, A.; Opsahl, T. et al. |
| Typ≠ | Centrality measure (network analysis) | Network analysis framework |
| 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 | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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. |
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