Bandingkan kaedah

	Pusat Kedekatan Berbobot	Pusat Darjah Berwajaran
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2010	2004
Pengasas	Opsahl, T.; Agneessens, F.; Skvoretz, J.	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
Jenis	Centrality measure (network analysis)	Centrality measure for weighted networks
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.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Alias	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness	node strength, strength centrality, weighted node degree, WDC
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.	Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.