Σύγκριση μεθόδων

	Σταθμισμένη Κεντρικότητα Ενδιάμεσης Θέσης	Σταθμισμένη Ανάλυση Κοινωνικών Δικτύων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2010	2004–2010
Δημιουργός	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)	Barrat, A.; Opsahl, T. et al.
Τύπος	Centrality measure (path-based)	Network analysis framework
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
Συναφείς	6	6
Σύνοψη	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.	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.