Bandingkan metode

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