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| Painotettu välisyyden keskeisyys× | Painotettu läheisyyskeskisyys× | |
|---|---|---|
| Tieteenala | Verkostoanalyysi | Verkostoanalyysi |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi | 2010 | 2010 |
| Kehittäjä≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) | Opsahl, T.; Agneessens, F.; Skvoretz, J. |
| Tyyppi≠ | Centrality measure (path-based) | Centrality measure (network analysis) |
| Alkuperäislähde≠ | Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ |
| Rinnakkaisnimet | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | 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 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. |
| ScholarGateAineisto ↗ |
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