Bandingkan kaedah

	Analisis Jaringan Sosial Berbobot	Sentraliti Kebersihan Berbobot
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004–2010	2010
Pengasas	Barrat, A.; Opsahl, T. et al.	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)
Jenis	Network analysis framework	Centrality measure (path-based)
Sumber perintis	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.	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 SNA, valued network analysis, tie-strength network analysis, weighted graph analysis	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)
Berkaitan	6	6
Ringkasan	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.	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.