Bandingkan kaedah

	Pusat Eigenvector Berbobot	Pusat Kedekatan Berbobot
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	1987 (binary); 2010 (weighted generalization)	2010
Pengasas	Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)	Opsahl, T.; Agneessens, F.; Skvoretz, J.
Jenis	Spectral centrality measure	Centrality measure (network analysis)
Sumber perintis	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.
Alias	WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness
Berkaitan	6	6
Ringkasan	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.