Módszerek összehasonlítása

	Súlyozott sajátvektor-központiság	Súlyozott közelségi központiság
Tudományterület	Hálózatelemzés	Hálózatelemzés
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1987 (binary); 2010 (weighted generalization)	2010
Megalkotó	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Opsahl, T.; Agneessens, F.; Skvoretz, J.
Típus	Spectral centrality measure	Centrality measure (network analysis)
Alapmű	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Alternatív nevek	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness
Kapcsolódó	6	6
Összefoglaló	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.	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.