Linganisha mbinu

	Ukuu wa Kiwango Kizito	Upekee wa Kati wa Uzito (Weighted Betweenness Centrality)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2004	2010
Mwanzilishi	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)
Aina	Centrality measure for weighted networks	Centrality measure (path-based)
Chanzo asilia	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.
Majina mbadala	node strength, strength centrality, weighted node degree, WDC	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)
Zinazohusiana	6	6
Muhtasari	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.	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.