Σύγκριση μεθόδων

	Σταθμισμένη Κεντρικότητα Ενδιάμεσης Θέσης	Σταθμισμένη Κεντρικότητα Εγγύτητας
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2010	2010
Δημιουργός	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)	Opsahl, T.; Agneessens, F.; Skvoretz, J.
Τύπος	Centrality measure (path-based)	Centrality measure (network analysis)
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.	Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Εναλλακτικές ονομασίες	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness
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 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.