Usporedite metode

	Uteženi izmerak središnjosti	Utegnuta blizinska centralnost
Područje	Analiza mreža	Analiza mreža
Obitelj	Machine learning	Machine learning
Godina nastanka	2010	2010
Tvorac	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)	Opsahl, T.; Agneessens, F.; Skvoretz, J.
Vrsta	Centrality measure (path-based)	Centrality measure (network analysis)
Temeljni izvor	Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.	Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Drugi nazivi	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness
Srodne	6	6
Sažetak	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.