Bandingkan kaedah

	Pusat Kedekatan Berbobot	Pusat Kesihatan Kekerabatan
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2010	1950 (formalized 1979)
Pengasas	Opsahl, T.; Agneessens, F.; Skvoretz, J.	Bavelas, A.; formalized by Freeman, L. C.
Jenis	Centrality measure (network analysis)	Node-level centrality index
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.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Alias	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Berkaitan	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.	Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.