Linganisha mbinu

	Ukaribu Uzito	Umuhimu wa Eigenvector
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010	1972
Mwanzilishi	Opsahl, T.; Agneessens, F.; Skvoretz, J.	Bonacich, P.
Aina	Centrality measure (network analysis)	Centrality measure
Chanzo asilia	Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Majina mbadala	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness	eigenvector centrality, EC, Bonacich centrality, power centrality
Zinazohusiana	6	6
Muhtasari	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.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.