Módszerek összehasonlítása

	Eigenvektor-központiság	A fokszám-központiság (Degree Centrality)
Tudományterület	Hálózatelemzés	Hálózatelemzés
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1972	1978
Megalkotó	Bonacich, P.	Freeman, L. C.
Típus	Centrality measure	Node-level centrality measure
Alapmű	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.	Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Alternatív nevek	eigenvector centrality, EC, Bonacich centrality, power centrality	node degree, degree score, DC, connectivity centrality
Kapcsolódó	6	6
Összefoglaló	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.	Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis.