Bandingkan kaedah

	Pusat Kedekatan Berbobot	Analisis Jaringan Sosial Berbobot
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2010	2004–2010
Pengasas	Opsahl, T.; Agneessens, F.; Skvoretz, J.	Barrat, A.; Opsahl, T. et al.
Jenis	Centrality measure (network analysis)	Network analysis framework
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	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
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 Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.