Bandingkan metode

	Sentralitas Antara Bobot	Sentralitas Kedekatan Berbobot
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2010	2010
Pencetus	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)	Opsahl, T.; Agneessens, F.; Skvoretz, J.
Tipe	Centrality measure (path-based)	Centrality measure (network analysis)
Sumber perintis	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.
Alias	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness
Terkait	6	6
Ringkasan	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.