Methoden vergleichen

	Gewichtete Eigenvektor-Zentralität	Gewichtete Gradzentralität
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	1987 (binary); 2010 (weighted generalization)	2004
Urheber	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.
Typ	Spectral centrality measure	Centrality measure for weighted networks
Wegweisende Quelle	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	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.
Aliasnamen	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	node strength, strength centrality, weighted node degree, WDC
Verwandt	6	6
Zusammenfassung	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 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.