Võrdle meetodeid

	Kaalutud omavektori tsentraalsus	Eigenvector Centrality
Valdkond	Võrgustikuanalüüs	Võrgustikuanalüüs
Perekond	Machine learning	Machine learning
Tekkeaasta	1987 (binary); 2010 (weighted generalization)	1972
Looja	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Bonacich, P.
Tüüp	Spectral centrality measure	Centrality measure
Algallikas	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Rööpnimetused	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	eigenvector centrality, EC, Bonacich centrality, power centrality
Seotud	6	6
Kokkuvõte	Weighted eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously.	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.